import {Request} from '../lib/request';
import {Response} from '../lib/response';
import {AWSError} from '../lib/error';
import {Service} from '../lib/service';
import {WaiterConfiguration} from '../lib/service';
import {ServiceConfigurationOptions} from '../lib/service';
import {ConfigBase as Config} from '../lib/config-base';
interface Blob {}
declare class SageMaker extends Service {
  /**
   * Constructs a service object. This object has one method for each API operation.
   */
  constructor(options?: SageMaker.Types.ClientConfiguration)
  config: Config & SageMaker.Types.ClientConfiguration;
  /**
   * Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  addAssociation(params: SageMaker.Types.AddAssociationRequest, callback?: (err: AWSError, data: SageMaker.Types.AddAssociationResponse) => void): Request<SageMaker.Types.AddAssociationResponse, AWSError>;
  /**
   * Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  addAssociation(callback?: (err: AWSError, data: SageMaker.Types.AddAssociationResponse) => void): Request<SageMaker.Types.AddAssociationResponse, AWSError>;
  /**
   * Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.  Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob    Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile. 
   */
  addTags(params: SageMaker.Types.AddTagsInput, callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.  Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob    Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile. 
   */
  addTags(callback?: (err: AWSError, data: SageMaker.Types.AddTagsOutput) => void): Request<SageMaker.Types.AddTagsOutput, AWSError>;
  /**
   * Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  associateTrialComponent(params: SageMaker.Types.AssociateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  associateTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.AssociateTrialComponentResponse) => void): Request<SageMaker.Types.AssociateTrialComponentResponse, AWSError>;
  /**
   * This action batch describes a list of versioned model packages
   */
  batchDescribeModelPackage(params: SageMaker.Types.BatchDescribeModelPackageInput, callback?: (err: AWSError, data: SageMaker.Types.BatchDescribeModelPackageOutput) => void): Request<SageMaker.Types.BatchDescribeModelPackageOutput, AWSError>;
  /**
   * This action batch describes a list of versioned model packages
   */
  batchDescribeModelPackage(callback?: (err: AWSError, data: SageMaker.Types.BatchDescribeModelPackageOutput) => void): Request<SageMaker.Types.BatchDescribeModelPackageOutput, AWSError>;
  /**
   * Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createAction(params: SageMaker.Types.CreateActionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateActionResponse) => void): Request<SageMaker.Types.CreateActionResponse, AWSError>;
  /**
   * Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createAction(callback?: (err: AWSError, data: SageMaker.Types.CreateActionResponse) => void): Request<SageMaker.Types.CreateActionResponse, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
   */
  createAlgorithm(params: SageMaker.Types.CreateAlgorithmInput, callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
   */
  createAlgorithm(callback?: (err: AWSError, data: SageMaker.Types.CreateAlgorithmOutput) => void): Request<SageMaker.Types.CreateAlgorithmOutput, AWSError>;
  /**
   * Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(params: SageMaker.Types.CreateAppRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
   */
  createApp(callback?: (err: AWSError, data: SageMaker.Types.CreateAppResponse) => void): Request<SageMaker.Types.CreateAppResponse, AWSError>;
  /**
   * Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
   */
  createAppImageConfig(params: SageMaker.Types.CreateAppImageConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAppImageConfigResponse) => void): Request<SageMaker.Types.CreateAppImageConfigResponse, AWSError>;
  /**
   * Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.
   */
  createAppImageConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateAppImageConfigResponse) => void): Request<SageMaker.Types.CreateAppImageConfigResponse, AWSError>;
  /**
   * Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createArtifact(params: SageMaker.Types.CreateArtifactRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateArtifactResponse) => void): Request<SageMaker.Types.CreateArtifactResponse, AWSError>;
  /**
   * Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createArtifact(callback?: (err: AWSError, data: SageMaker.Types.CreateArtifactResponse) => void): Request<SageMaker.Types.CreateArtifactResponse, AWSError>;
  /**
   * Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.  We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.  CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.  You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
   */
  createAutoMLJob(params: SageMaker.Types.CreateAutoMLJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * Creates an Autopilot job also referred to as Autopilot experiment or AutoML job. An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide.  We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.  CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.  You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.
   */
  createAutoMLJob(callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobResponse) => void): Request<SageMaker.Types.CreateAutoMLJobResponse, AWSError>;
  /**
   * Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2. An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide. AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.   CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.  CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.  For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig. You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
   */
  createAutoMLJobV2(params: SageMaker.Types.CreateAutoMLJobV2Request, callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobV2Response) => void): Request<SageMaker.Types.CreateAutoMLJobV2Response, AWSError>;
  /**
   * Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2. An AutoML job in SageMaker is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment. For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker developer guide. AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.   CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.  CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning). Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.  For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig. You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
   */
  createAutoMLJobV2(callback?: (err: AWSError, data: SageMaker.Types.CreateAutoMLJobV2Response) => void): Request<SageMaker.Types.CreateAutoMLJobV2Response, AWSError>;
  /**
   * Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
   */
  createCluster(params: SageMaker.Types.CreateClusterRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateClusterResponse) => void): Request<SageMaker.Types.CreateClusterResponse, AWSError>;
  /**
   * Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.
   */
  createCluster(callback?: (err: AWSError, data: SageMaker.Types.CreateClusterResponse) => void): Request<SageMaker.Types.CreateClusterResponse, AWSError>;
  /**
   * Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
   */
  createCodeRepository(params: SageMaker.Types.CreateCodeRepositoryInput, callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with. The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.
   */
  createCodeRepository(callback?: (err: AWSError, data: SageMaker.Types.CreateCodeRepositoryOutput) => void): Request<SageMaker.Types.CreateCodeRepositoryOutput, AWSError>;
  /**
   * Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.  If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following:   A name for the compilation job    Information about the input model artifacts    The output location for the compiled model and the device (target) that the model runs on    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  createCompilationJob(params: SageMaker.Types.CreateCompilationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.  If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource. In the request body, you provide the following:   A name for the compilation job    Information about the input model artifacts    The output location for the compiled model and the device (target) that the model runs on    The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.    You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job. To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  createCompilationJob(callback?: (err: AWSError, data: SageMaker.Types.CreateCompilationJobResponse) => void): Request<SageMaker.Types.CreateCompilationJobResponse, AWSError>;
  /**
   * Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createContext(params: SageMaker.Types.CreateContextRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateContextResponse) => void): Request<SageMaker.Types.CreateContextResponse, AWSError>;
  /**
   * Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.
   */
  createContext(callback?: (err: AWSError, data: SageMaker.Types.CreateContextResponse) => void): Request<SageMaker.Types.CreateContextResponse, AWSError>;
  /**
   * Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createDataQualityJobDefinition(params: SageMaker.Types.CreateDataQualityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createDataQualityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a device fleet.
   */
  createDeviceFleet(params: SageMaker.Types.CreateDeviceFleetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates a device fleet.
   */
  createDeviceFleet(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.  EFS storage  When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.  VPC configuration  All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:    PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.    VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.  For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
   */
  createDomain(params: SageMaker.Types.CreateDomainRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.  EFS storage  When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files. SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.  VPC configuration  All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:    PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker, which allows internet access. This is the default value.    VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway. When internet access is disabled, you won't be able to run a Amazon SageMaker Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker API and runtime or a NAT gateway and your security groups allow outbound connections.    NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker Studio app successfully.  For more information, see Connect Amazon SageMaker Studio Notebooks to Resources in a VPC.
   */
  createDomain(callback?: (err: AWSError, data: SageMaker.Types.CreateDomainResponse) => void): Request<SageMaker.Types.CreateDomainResponse, AWSError>;
  /**
   * Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
   */
  createEdgeDeploymentPlan(params: SageMaker.Types.CreateEdgeDeploymentPlanRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateEdgeDeploymentPlanResponse) => void): Request<SageMaker.Types.CreateEdgeDeploymentPlanResponse, AWSError>;
  /**
   * Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
   */
  createEdgeDeploymentPlan(callback?: (err: AWSError, data: SageMaker.Types.CreateEdgeDeploymentPlanResponse) => void): Request<SageMaker.Types.CreateEdgeDeploymentPlanResponse, AWSError>;
  /**
   * Creates a new stage in an existing edge deployment plan.
   */
  createEdgeDeploymentStage(params: SageMaker.Types.CreateEdgeDeploymentStageRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates a new stage in an existing edge deployment plan.
   */
  createEdgeDeploymentStage(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
   */
  createEdgePackagingJob(params: SageMaker.Types.CreateEdgePackagingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.
   */
  createEdgePackagingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.   Use this API to deploy models using SageMaker hosting services.    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.  The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.  When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.  When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.   To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.   Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:   "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]   "Resource": [   "arn:aws:sagemaker:region:account-id:endpoint/endpointName"   "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"   ]  For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.   
   */
  createEndpoint(params: SageMaker.Types.CreateEndpointInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.   Use this API to deploy models using SageMaker hosting services.    You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.  The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.  When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.  When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API. If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.   To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.    Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.   Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:   "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]   "Resource": [   "arn:aws:sagemaker:region:account-id:endpoint/endpointName"   "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"   ]  For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.   
   */
  createEndpoint(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointOutput) => void): Request<SageMaker.Types.CreateEndpointOutput, AWSError>;
  /**
   * Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(params: SageMaker.Types.CreateEndpointConfigInput, callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.   Use this API if you want to use SageMaker hosting services to deploy models into production.   In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.   When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting  Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read. 
   */
  createEndpointConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateEndpointConfigOutput) => void): Request<SageMaker.Types.CreateEndpointConfigOutput, AWSError>;
  /**
   * Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.  In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.  The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(params: SageMaker.Types.CreateExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.  In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.  The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to experiments, trials, trial components and then use the Search API to search for the tags. To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API. To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.
   */
  createExperiment(callback?: (err: AWSError, data: SageMaker.Types.CreateExperimentResponse) => void): Request<SageMaker.Types.CreateExperimentResponse, AWSError>;
  /**
   * Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.  The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account. Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType.  You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup. 
   */
  createFeatureGroup(params: SageMaker.Types.CreateFeatureGroupRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateFeatureGroupResponse) => void): Request<SageMaker.Types.CreateFeatureGroupResponse, AWSError>;
  /**
   * Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.  The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account. Note that it can take approximately 10-15 minutes to provision an OnlineStore FeatureGroup with the InMemory StorageType.  You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup. 
   */
  createFeatureGroup(callback?: (err: AWSError, data: SageMaker.Types.CreateFeatureGroupResponse) => void): Request<SageMaker.Types.CreateFeatureGroupResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(params: SageMaker.Types.CreateFlowDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * Creates a flow definition.
   */
  createFlowDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateFlowDefinitionResponse) => void): Request<SageMaker.Types.CreateFlowDefinitionResponse, AWSError>;
  /**
   * Create a hub.
   */
  createHub(params: SageMaker.Types.CreateHubRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHubResponse) => void): Request<SageMaker.Types.CreateHubResponse, AWSError>;
  /**
   * Create a hub.
   */
  createHub(callback?: (err: AWSError, data: SageMaker.Types.CreateHubResponse) => void): Request<SageMaker.Types.CreateHubResponse, AWSError>;
  /**
   * Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
   */
  createHubContentReference(params: SageMaker.Types.CreateHubContentReferenceRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHubContentReferenceResponse) => void): Request<SageMaker.Types.CreateHubContentReferenceResponse, AWSError>;
  /**
   * Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.
   */
  createHubContentReference(callback?: (err: AWSError, data: SageMaker.Types.CreateHubContentReferenceResponse) => void): Request<SageMaker.Types.CreateHubContentReferenceResponse, AWSError>;
  /**
   * Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
   */
  createHumanTaskUi(params: SageMaker.Types.CreateHumanTaskUiRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.
   */
  createHumanTaskUi(callback?: (err: AWSError, data: SageMaker.Types.CreateHumanTaskUiResponse) => void): Request<SageMaker.Types.CreateHumanTaskUiResponse, AWSError>;
  /**
   * Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.  Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error. 
   */
  createHyperParameterTuningJob(params: SageMaker.Types.CreateHyperParameterTuningJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.  Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error. 
   */
  createHyperParameterTuningJob(callback?: (err: AWSError, data: SageMaker.Types.CreateHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.CreateHyperParameterTuningJobResponse, AWSError>;
  /**
   * Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker image.
   */
  createImage(params: SageMaker.Types.CreateImageRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateImageResponse) => void): Request<SageMaker.Types.CreateImageResponse, AWSError>;
  /**
   * Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon ECR. For more information, see Bring your own SageMaker image.
   */
  createImage(callback?: (err: AWSError, data: SageMaker.Types.CreateImageResponse) => void): Request<SageMaker.Types.CreateImageResponse, AWSError>;
  /**
   * Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
   */
  createImageVersion(params: SageMaker.Types.CreateImageVersionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateImageVersionResponse) => void): Request<SageMaker.Types.CreateImageVersionResponse, AWSError>;
  /**
   * Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon ECR container image specified by BaseImage.
   */
  createImageVersion(callback?: (err: AWSError, data: SageMaker.Types.CreateImageVersionResponse) => void): Request<SageMaker.Types.CreateImageVersionResponse, AWSError>;
  /**
   * Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
   */
  createInferenceComponent(params: SageMaker.Types.CreateInferenceComponentInput, callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceComponentOutput) => void): Request<SageMaker.Types.CreateInferenceComponentOutput, AWSError>;
  /**
   * Creates an inference component, which is a SageMaker hosting object that you can use to deploy a model to an endpoint. In the inference component settings, you specify the model, the endpoint, and how the model utilizes the resources that the endpoint hosts. You can optimize resource utilization by tailoring how the required CPU cores, accelerators, and memory are allocated. You can deploy multiple inference components to an endpoint, where each inference component contains one model and the resource utilization needs for that individual model. After you deploy an inference component, you can directly invoke the associated model when you use the InvokeEndpoint API action.
   */
  createInferenceComponent(callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceComponentOutput) => void): Request<SageMaker.Types.CreateInferenceComponentOutput, AWSError>;
  /**
   *  Creates an inference experiment using the configurations specified in the request.   Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.   Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.   While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests. 
   */
  createInferenceExperiment(params: SageMaker.Types.CreateInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceExperimentResponse) => void): Request<SageMaker.Types.CreateInferenceExperimentResponse, AWSError>;
  /**
   *  Creates an inference experiment using the configurations specified in the request.   Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.   Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.   While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests. 
   */
  createInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceExperimentResponse) => void): Request<SageMaker.Types.CreateInferenceExperimentResponse, AWSError>;
  /**
   * Starts a recommendation job. You can create either an instance recommendation or load test job.
   */
  createInferenceRecommendationsJob(params: SageMaker.Types.CreateInferenceRecommendationsJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceRecommendationsJobResponse) => void): Request<SageMaker.Types.CreateInferenceRecommendationsJobResponse, AWSError>;
  /**
   * Starts a recommendation job. You can create either an instance recommendation or load test job.
   */
  createInferenceRecommendationsJob(callback?: (err: AWSError, data: SageMaker.Types.CreateInferenceRecommendationsJobResponse) => void): Request<SageMaker.Types.CreateInferenceRecommendationsJobResponse, AWSError>;
  /**
   * Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.  You can select your workforce from one of three providers:   A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.   One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.    The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.   You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API)  in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
   */
  createLabelingJob(params: SageMaker.Types.CreateLabelingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.  You can select your workforce from one of three providers:   A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.   One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.    The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.   You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling. The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data. The output can be used as the manifest file for another labeling job or as training data for your machine learning models. You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API)  in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.
   */
  createLabelingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateLabelingJobResponse) => void): Request<SageMaker.Types.CreateLabelingJobResponse, AWSError>;
  /**
   * Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
   */
  createMlflowTrackingServer(params: SageMaker.Types.CreateMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateMlflowTrackingServerResponse) => void): Request<SageMaker.Types.CreateMlflowTrackingServerResponse, AWSError>;
  /**
   * Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store. For more information, see Create an MLflow Tracking Server.
   */
  createMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.CreateMlflowTrackingServerResponse) => void): Request<SageMaker.Types.CreateMlflowTrackingServerResponse, AWSError>;
  /**
   * Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.  To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
   */
  createModel(params: SageMaker.Types.CreateModelInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelOutput) => void): Request<SageMaker.Types.CreateModelOutput, AWSError>;
  /**
   * Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions. Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job. To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.  To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location. In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.
   */
  createModel(callback?: (err: AWSError, data: SageMaker.Types.CreateModelOutput) => void): Request<SageMaker.Types.CreateModelOutput, AWSError>;
  /**
   * Creates the definition for a model bias job.
   */
  createModelBiasJobDefinition(params: SageMaker.Types.CreateModelBiasJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateModelBiasJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelBiasJobDefinitionResponse, AWSError>;
  /**
   * Creates the definition for a model bias job.
   */
  createModelBiasJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateModelBiasJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelBiasJobDefinitionResponse, AWSError>;
  /**
   * Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.
   */
  createModelCard(params: SageMaker.Types.CreateModelCardRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateModelCardResponse) => void): Request<SageMaker.Types.CreateModelCardResponse, AWSError>;
  /**
   * Creates an Amazon SageMaker Model Card. For information about how to use model cards, see Amazon SageMaker Model Card.
   */
  createModelCard(callback?: (err: AWSError, data: SageMaker.Types.CreateModelCardResponse) => void): Request<SageMaker.Types.CreateModelCardResponse, AWSError>;
  /**
   * Creates an Amazon SageMaker Model Card export job.
   */
  createModelCardExportJob(params: SageMaker.Types.CreateModelCardExportJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateModelCardExportJobResponse) => void): Request<SageMaker.Types.CreateModelCardExportJobResponse, AWSError>;
  /**
   * Creates an Amazon SageMaker Model Card export job.
   */
  createModelCardExportJob(callback?: (err: AWSError, data: SageMaker.Types.CreateModelCardExportJobResponse) => void): Request<SageMaker.Types.CreateModelCardExportJobResponse, AWSError>;
  /**
   * Creates the definition for a model explainability job.
   */
  createModelExplainabilityJobDefinition(params: SageMaker.Types.CreateModelExplainabilityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateModelExplainabilityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelExplainabilityJobDefinitionResponse, AWSError>;
  /**
   * Creates the definition for a model explainability job.
   */
  createModelExplainabilityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateModelExplainabilityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelExplainabilityJobDefinitionResponse, AWSError>;
  /**
   * Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.  There are two types of model packages:   Versioned - a model that is part of a model group in the model registry.   Unversioned - a model package that is not part of a model group.   
   */
  createModelPackage(params: SageMaker.Types.CreateModelPackageInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageOutput) => void): Request<SageMaker.Types.CreateModelPackageOutput, AWSError>;
  /**
   * Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker. To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.  There are two types of model packages:   Versioned - a model that is part of a model group in the model registry.   Unversioned - a model package that is not part of a model group.   
   */
  createModelPackage(callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageOutput) => void): Request<SageMaker.Types.CreateModelPackageOutput, AWSError>;
  /**
   * Creates a model group. A model group contains a group of model versions.
   */
  createModelPackageGroup(params: SageMaker.Types.CreateModelPackageGroupInput, callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageGroupOutput) => void): Request<SageMaker.Types.CreateModelPackageGroupOutput, AWSError>;
  /**
   * Creates a model group. A model group contains a group of model versions.
   */
  createModelPackageGroup(callback?: (err: AWSError, data: SageMaker.Types.CreateModelPackageGroupOutput) => void): Request<SageMaker.Types.CreateModelPackageGroupOutput, AWSError>;
  /**
   * Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createModelQualityJobDefinition(params: SageMaker.Types.CreateModelQualityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateModelQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.
   */
  createModelQualityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.CreateModelQualityJobDefinitionResponse) => void): Request<SageMaker.Types.CreateModelQualityJobDefinitionResponse, AWSError>;
  /**
   * Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
   */
  createMonitoringSchedule(params: SageMaker.Types.CreateMonitoringScheduleRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateMonitoringScheduleResponse) => void): Request<SageMaker.Types.CreateMonitoringScheduleResponse, AWSError>;
  /**
   * Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
   */
  createMonitoringSchedule(callback?: (err: AWSError, data: SageMaker.Types.CreateMonitoringScheduleResponse) => void): Request<SageMaker.Types.CreateMonitoringScheduleResponse, AWSError>;
  /**
   * Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.  In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.  SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.  After receiving the request, SageMaker does the following:   Creates a network interface in the SageMaker VPC.   (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.   Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.   After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.  For more information, see How It Works. 
   */
  createNotebookInstance(params: SageMaker.Types.CreateNotebookInstanceInput, callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceOutput, AWSError>;
  /**
   * Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.  In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.  SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.  After receiving the request, SageMaker does the following:   Creates a network interface in the SageMaker VPC.   (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.   Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.   After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it. After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.  For more information, see How It Works. 
   */
  createNotebookInstance(callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceOutput, AWSError>;
  /**
   * Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
   */
  createNotebookInstanceLifecycleConfig(params: SageMaker.Types.CreateNotebookInstanceLifecycleConfigInput, callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance. Each lifecycle configuration script has a limit of 16384 characters. The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin. View Amazon CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook]. Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
   */
  createNotebookInstanceLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.CreateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
   */
  createOptimizationJob(params: SageMaker.Types.CreateOptimizationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateOptimizationJobResponse) => void): Request<SageMaker.Types.CreateOptimizationJobResponse, AWSError>;
  /**
   * Creates a job that optimizes a model for inference performance. To create the job, you provide the location of a source model, and you provide the settings for the optimization techniques that you want the job to apply. When the job completes successfully, SageMaker uploads the new optimized model to the output destination that you specify. For more information about how to use this action, and about the supported optimization techniques, see Optimize model inference with Amazon SageMaker.
   */
  createOptimizationJob(callback?: (err: AWSError, data: SageMaker.Types.CreateOptimizationJobResponse) => void): Request<SageMaker.Types.CreateOptimizationJobResponse, AWSError>;
  /**
   * Creates a pipeline using a JSON pipeline definition.
   */
  createPipeline(params: SageMaker.Types.CreatePipelineRequest, callback?: (err: AWSError, data: SageMaker.Types.CreatePipelineResponse) => void): Request<SageMaker.Types.CreatePipelineResponse, AWSError>;
  /**
   * Creates a pipeline using a JSON pipeline definition.
   */
  createPipeline(callback?: (err: AWSError, data: SageMaker.Types.CreatePipelineResponse) => void): Request<SageMaker.Types.CreatePipelineResponse, AWSError>;
  /**
   * Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.  The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .  The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. 
   */
  createPresignedDomainUrl(params: SageMaker.Types.CreatePresignedDomainUrlRequest, callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedDomainUrlResponse) => void): Request<SageMaker.Types.CreatePresignedDomainUrlResponse, AWSError>;
  /**
   * Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to the domain, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System volume. This operation can only be called when the authentication mode equals IAM.  The IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app. You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to Amazon SageMaker Studio Through an Interface VPC Endpoint .  The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page. 
   */
  createPresignedDomainUrl(callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedDomainUrlResponse) => void): Request<SageMaker.Types.CreatePresignedDomainUrlResponse, AWSError>;
  /**
   * Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
   */
  createPresignedMlflowTrackingServerUrl(params: SageMaker.Types.CreatePresignedMlflowTrackingServerUrlRequest, callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedMlflowTrackingServerUrlResponse) => void): Request<SageMaker.Types.CreatePresignedMlflowTrackingServerUrlResponse, AWSError>;
  /**
   * Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server. For more information, see Launch the MLflow UI using a presigned URL.
   */
  createPresignedMlflowTrackingServerUrl(callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedMlflowTrackingServerUrlResponse) => void): Request<SageMaker.Types.CreatePresignedMlflowTrackingServerUrlResponse, AWSError>;
  /**
   * Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.  The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.  The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page. 
   */
  createPresignedNotebookInstanceUrl(params: SageMaker.Types.CreatePresignedNotebookInstanceUrlInput, callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput) => void): Request<SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput, AWSError>;
  /**
   * Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.  The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance. You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.  The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page. 
   */
  createPresignedNotebookInstanceUrl(callback?: (err: AWSError, data: SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput) => void): Request<SageMaker.Types.CreatePresignedNotebookInstanceUrlOutput, AWSError>;
  /**
   * Creates a processing job.
   */
  createProcessingJob(params: SageMaker.Types.CreateProcessingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateProcessingJobResponse) => void): Request<SageMaker.Types.CreateProcessingJobResponse, AWSError>;
  /**
   * Creates a processing job.
   */
  createProcessingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateProcessingJobResponse) => void): Request<SageMaker.Types.CreateProcessingJobResponse, AWSError>;
  /**
   * Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
   */
  createProject(params: SageMaker.Types.CreateProjectInput, callback?: (err: AWSError, data: SageMaker.Types.CreateProjectOutput) => void): Request<SageMaker.Types.CreateProjectOutput, AWSError>;
  /**
   * Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.
   */
  createProject(callback?: (err: AWSError, data: SageMaker.Types.CreateProjectOutput) => void): Request<SageMaker.Types.CreateProjectOutput, AWSError>;
  /**
   * Creates a private space or a space used for real time collaboration in a domain.
   */
  createSpace(params: SageMaker.Types.CreateSpaceRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateSpaceResponse) => void): Request<SageMaker.Types.CreateSpaceResponse, AWSError>;
  /**
   * Creates a private space or a space used for real time collaboration in a domain.
   */
  createSpace(callback?: (err: AWSError, data: SageMaker.Types.CreateSpaceResponse) => void): Request<SageMaker.Types.CreateSpaceResponse, AWSError>;
  /**
   * Creates a new Amazon SageMaker Studio Lifecycle Configuration.
   */
  createStudioLifecycleConfig(params: SageMaker.Types.CreateStudioLifecycleConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateStudioLifecycleConfigResponse) => void): Request<SageMaker.Types.CreateStudioLifecycleConfigResponse, AWSError>;
  /**
   * Creates a new Amazon SageMaker Studio Lifecycle Configuration.
   */
  createStudioLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.CreateStudioLifecycleConfigResponse) => void): Request<SageMaker.Types.CreateStudioLifecycleConfigResponse, AWSError>;
  /**
   * Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.  If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.  In the request body, you provide the following:     AlgorithmSpecification - Identifies the training algorithm to use.     HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.   Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.     InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.    OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.     ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.     EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.     RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.     StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.     Environment - The environment variables to set in the Docker container.    RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.    For more information about SageMaker, see How It Works. 
   */
  createTrainingJob(params: SageMaker.Types.CreateTrainingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateTrainingJobResponse) => void): Request<SageMaker.Types.CreateTrainingJobResponse, AWSError>;
  /**
   * Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.  If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.  In the request body, you provide the following:     AlgorithmSpecification - Identifies the training algorithm to use.     HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.   Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.     InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.    OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.     ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.     EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.     RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.     StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.     Environment - The environment variables to set in the Docker container.    RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.    For more information about SageMaker, see How It Works. 
   */
  createTrainingJob(callback?: (err: AWSError, data: SageMaker.Types.CreateTrainingJobResponse) => void): Request<SageMaker.Types.CreateTrainingJobResponse, AWSError>;
  /**
   * Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following:    TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.    ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.    TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.    TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.    TransformResources - Identifies the ML compute instances for the transform job.   For more information about how batch transformation works, see Batch Transform.
   */
  createTransformJob(params: SageMaker.Types.CreateTransformJobRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateTransformJobResponse) => void): Request<SageMaker.Types.CreateTransformJobResponse, AWSError>;
  /**
   * Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify. To perform batch transformations, you create a transform job and use the data that you have readily available. In the request body, you provide the following:    TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.    ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.    TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.    TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.    TransformResources - Identifies the ML compute instances for the transform job.   For more information about how batch transformation works, see Batch Transform.
   */
  createTransformJob(callback?: (err: AWSError, data: SageMaker.Types.CreateTransformJobResponse) => void): Request<SageMaker.Types.CreateTransformJobResponse, AWSError>;
  /**
   * Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
   */
  createTrial(params: SageMaker.Types.CreateTrialRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateTrialResponse) => void): Request<SageMaker.Types.CreateTrialResponse, AWSError>;
  /**
   * Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial and then use the Search API to search for the tags. To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.
   */
  createTrial(callback?: (err: AWSError, data: SageMaker.Types.CreateTrialResponse) => void): Request<SageMaker.Types.CreateTrialResponse, AWSError>;
  /**
   * Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.
   */
  createTrialComponent(params: SageMaker.Types.CreateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateTrialComponentResponse) => void): Request<SageMaker.Types.CreateTrialComponentResponse, AWSError>;
  /**
   * Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials. Trial components include pre-processing jobs, training jobs, and batch transform jobs. When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK. You can add tags to a trial component and then use the Search API to search for the tags.
   */
  createTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.CreateTrialComponentResponse) => void): Request<SageMaker.Types.CreateTrialComponentResponse, AWSError>;
  /**
   * Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory. 
   */
  createUserProfile(params: SageMaker.Types.CreateUserProfileRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateUserProfileResponse) => void): Request<SageMaker.Types.CreateUserProfileResponse, AWSError>;
  /**
   * Creates a user profile. A user profile represents a single user within a domain, and is the main way to reference a "person" for the purposes of sharing, reporting, and other user-oriented features. This entity is created when a user onboards to a domain. If an administrator invites a person by email or imports them from IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System home directory. 
   */
  createUserProfile(callback?: (err: AWSError, data: SageMaker.Types.CreateUserProfileResponse) => void): Request<SageMaker.Types.CreateUserProfileResponse, AWSError>;
  /**
   * Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see  Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see  Create a Private Workforce (OIDC IdP).
   */
  createWorkforce(params: SageMaker.Types.CreateWorkforceRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateWorkforceResponse) => void): Request<SageMaker.Types.CreateWorkforceResponse, AWSError>;
  /**
   * Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce. To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see  Create a Private Workforce (Amazon Cognito). To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see  Create a Private Workforce (OIDC IdP).
   */
  createWorkforce(callback?: (err: AWSError, data: SageMaker.Types.CreateWorkforceResponse) => void): Request<SageMaker.Types.CreateWorkforceResponse, AWSError>;
  /**
   * Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.
   */
  createWorkteam(params: SageMaker.Types.CreateWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.CreateWorkteamResponse) => void): Request<SageMaker.Types.CreateWorkteamResponse, AWSError>;
  /**
   * Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team. You cannot create more than 25 work teams in an account and region.
   */
  createWorkteam(callback?: (err: AWSError, data: SageMaker.Types.CreateWorkteamResponse) => void): Request<SageMaker.Types.CreateWorkteamResponse, AWSError>;
  /**
   * Deletes an action.
   */
  deleteAction(params: SageMaker.Types.DeleteActionRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteActionResponse) => void): Request<SageMaker.Types.DeleteActionResponse, AWSError>;
  /**
   * Deletes an action.
   */
  deleteAction(callback?: (err: AWSError, data: SageMaker.Types.DeleteActionResponse) => void): Request<SageMaker.Types.DeleteActionResponse, AWSError>;
  /**
   * Removes the specified algorithm from your account.
   */
  deleteAlgorithm(params: SageMaker.Types.DeleteAlgorithmInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Removes the specified algorithm from your account.
   */
  deleteAlgorithm(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to stop and delete an app.
   */
  deleteApp(params: SageMaker.Types.DeleteAppRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to stop and delete an app.
   */
  deleteApp(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an AppImageConfig.
   */
  deleteAppImageConfig(params: SageMaker.Types.DeleteAppImageConfigRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an AppImageConfig.
   */
  deleteAppImageConfig(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an artifact. Either ArtifactArn or Source must be specified.
   */
  deleteArtifact(params: SageMaker.Types.DeleteArtifactRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteArtifactResponse) => void): Request<SageMaker.Types.DeleteArtifactResponse, AWSError>;
  /**
   * Deletes an artifact. Either ArtifactArn or Source must be specified.
   */
  deleteArtifact(callback?: (err: AWSError, data: SageMaker.Types.DeleteArtifactResponse) => void): Request<SageMaker.Types.DeleteArtifactResponse, AWSError>;
  /**
   * Deletes an association.
   */
  deleteAssociation(params: SageMaker.Types.DeleteAssociationRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteAssociationResponse) => void): Request<SageMaker.Types.DeleteAssociationResponse, AWSError>;
  /**
   * Deletes an association.
   */
  deleteAssociation(callback?: (err: AWSError, data: SageMaker.Types.DeleteAssociationResponse) => void): Request<SageMaker.Types.DeleteAssociationResponse, AWSError>;
  /**
   * Delete a SageMaker HyperPod cluster.
   */
  deleteCluster(params: SageMaker.Types.DeleteClusterRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteClusterResponse) => void): Request<SageMaker.Types.DeleteClusterResponse, AWSError>;
  /**
   * Delete a SageMaker HyperPod cluster.
   */
  deleteCluster(callback?: (err: AWSError, data: SageMaker.Types.DeleteClusterResponse) => void): Request<SageMaker.Types.DeleteClusterResponse, AWSError>;
  /**
   * Deletes the specified Git repository from your account.
   */
  deleteCodeRepository(params: SageMaker.Types.DeleteCodeRepositoryInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified Git repository from your account.
   */
  deleteCodeRepository(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role. You can delete a compilation job only if its current status is COMPLETED, FAILED, or STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and then delete it after its status becomes STOPPED.
   */
  deleteCompilationJob(params: SageMaker.Types.DeleteCompilationJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified compilation job. This action deletes only the compilation job resource in Amazon SageMaker. It doesn't delete other resources that are related to that job, such as the model artifacts that the job creates, the compilation logs in CloudWatch, the compiled model, or the IAM role. You can delete a compilation job only if its current status is COMPLETED, FAILED, or STOPPED. If the job status is STARTING or INPROGRESS, stop the job, and then delete it after its status becomes STOPPED.
   */
  deleteCompilationJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an context.
   */
  deleteContext(params: SageMaker.Types.DeleteContextRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteContextResponse) => void): Request<SageMaker.Types.DeleteContextResponse, AWSError>;
  /**
   * Deletes an context.
   */
  deleteContext(callback?: (err: AWSError, data: SageMaker.Types.DeleteContextResponse) => void): Request<SageMaker.Types.DeleteContextResponse, AWSError>;
  /**
   * Deletes a data quality monitoring job definition.
   */
  deleteDataQualityJobDefinition(params: SageMaker.Types.DeleteDataQualityJobDefinitionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a data quality monitoring job definition.
   */
  deleteDataQualityJobDefinition(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a fleet.
   */
  deleteDeviceFleet(params: SageMaker.Types.DeleteDeviceFleetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a fleet.
   */
  deleteDeviceFleet(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts. 
   */
  deleteDomain(params: SageMaker.Types.DeleteDomainRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts. 
   */
  deleteDomain(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
   */
  deleteEdgeDeploymentPlan(params: SageMaker.Types.DeleteEdgeDeploymentPlanRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
   */
  deleteEdgeDeploymentPlan(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
   */
  deleteEdgeDeploymentStage(params: SageMaker.Types.DeleteEdgeDeploymentStageRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete a stage in an edge deployment plan if (and only if) the stage is inactive.
   */
  deleteEdgeDeploymentStage(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.  SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call. When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your  ExecutionRoleArn , otherwise SageMaker cannot delete these resources.
   */
  deleteEndpoint(params: SageMaker.Types.DeleteEndpointInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.  SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call. When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your  ExecutionRoleArn , otherwise SageMaker cannot delete these resources.
   */
  deleteEndpoint(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.  You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
   */
  deleteEndpointConfig(params: SageMaker.Types.DeleteEndpointConfigInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.  You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges.
   */
  deleteEndpointConfig(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
   */
  deleteExperiment(params: SageMaker.Types.DeleteExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteExperimentResponse) => void): Request<SageMaker.Types.DeleteExperimentResponse, AWSError>;
  /**
   * Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.
   */
  deleteExperiment(callback?: (err: AWSError, data: SageMaker.Types.DeleteExperimentResponse) => void): Request<SageMaker.Types.DeleteExperimentResponse, AWSError>;
  /**
   * Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.  Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.  Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.
   */
  deleteFeatureGroup(params: SageMaker.Types.DeleteFeatureGroupRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.  Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.  Note that it can take approximately 10-15 minutes to delete an OnlineStore FeatureGroup with the InMemory StorageType.
   */
  deleteFeatureGroup(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified flow definition.
   */
  deleteFlowDefinition(params: SageMaker.Types.DeleteFlowDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteFlowDefinitionResponse) => void): Request<SageMaker.Types.DeleteFlowDefinitionResponse, AWSError>;
  /**
   * Deletes the specified flow definition.
   */
  deleteFlowDefinition(callback?: (err: AWSError, data: SageMaker.Types.DeleteFlowDefinitionResponse) => void): Request<SageMaker.Types.DeleteFlowDefinitionResponse, AWSError>;
  /**
   * Delete a hub.
   */
  deleteHub(params: SageMaker.Types.DeleteHubRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete a hub.
   */
  deleteHub(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete the contents of a hub.
   */
  deleteHubContent(params: SageMaker.Types.DeleteHubContentRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete the contents of a hub.
   */
  deleteHubContent(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete a hub content reference in order to remove a model from a private hub.
   */
  deleteHubContentReference(params: SageMaker.Types.DeleteHubContentReferenceRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete a hub content reference in order to remove a model from a private hub.
   */
  deleteHubContentReference(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Use this operation to delete a human task user interface (worker task template).  To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
   */
  deleteHumanTaskUi(params: SageMaker.Types.DeleteHumanTaskUiRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteHumanTaskUiResponse) => void): Request<SageMaker.Types.DeleteHumanTaskUiResponse, AWSError>;
  /**
   * Use this operation to delete a human task user interface (worker task template).  To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
   */
  deleteHumanTaskUi(callback?: (err: AWSError, data: SageMaker.Types.DeleteHumanTaskUiResponse) => void): Request<SageMaker.Types.DeleteHumanTaskUiResponse, AWSError>;
  /**
   * Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
   */
  deleteHyperParameterTuningJob(params: SageMaker.Types.DeleteHyperParameterTuningJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a hyperparameter tuning job. The DeleteHyperParameterTuningJob API deletes only the tuning job entry that was created in SageMaker when you called the CreateHyperParameterTuningJob API. It does not delete training jobs, artifacts, or the IAM role that you specified when creating the model.
   */
  deleteHyperParameterTuningJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
   */
  deleteImage(params: SageMaker.Types.DeleteImageRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteImageResponse) => void): Request<SageMaker.Types.DeleteImageResponse, AWSError>;
  /**
   * Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
   */
  deleteImage(callback?: (err: AWSError, data: SageMaker.Types.DeleteImageResponse) => void): Request<SageMaker.Types.DeleteImageResponse, AWSError>;
  /**
   * Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
   */
  deleteImageVersion(params: SageMaker.Types.DeleteImageVersionRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteImageVersionResponse) => void): Request<SageMaker.Types.DeleteImageVersionResponse, AWSError>;
  /**
   * Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
   */
  deleteImageVersion(callback?: (err: AWSError, data: SageMaker.Types.DeleteImageVersionResponse) => void): Request<SageMaker.Types.DeleteImageVersionResponse, AWSError>;
  /**
   * Deletes an inference component.
   */
  deleteInferenceComponent(params: SageMaker.Types.DeleteInferenceComponentInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an inference component.
   */
  deleteInferenceComponent(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an inference experiment.   This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.  
   */
  deleteInferenceExperiment(params: SageMaker.Types.DeleteInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteInferenceExperimentResponse) => void): Request<SageMaker.Types.DeleteInferenceExperimentResponse, AWSError>;
  /**
   * Deletes an inference experiment.   This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.  
   */
  deleteInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.DeleteInferenceExperimentResponse) => void): Request<SageMaker.Types.DeleteInferenceExperimentResponse, AWSError>;
  /**
   * Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
   */
  deleteMlflowTrackingServer(params: SageMaker.Types.DeleteMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteMlflowTrackingServerResponse) => void): Request<SageMaker.Types.DeleteMlflowTrackingServerResponse, AWSError>;
  /**
   * Deletes an MLflow Tracking Server. For more information, see Clean up MLflow resources.
   */
  deleteMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.DeleteMlflowTrackingServerResponse) => void): Request<SageMaker.Types.DeleteMlflowTrackingServerResponse, AWSError>;
  /**
   * Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model. 
   */
  deleteModel(params: SageMaker.Types.DeleteModelInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model. 
   */
  deleteModel(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker model bias job definition.
   */
  deleteModelBiasJobDefinition(params: SageMaker.Types.DeleteModelBiasJobDefinitionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker model bias job definition.
   */
  deleteModelBiasJobDefinition(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker Model Card.
   */
  deleteModelCard(params: SageMaker.Types.DeleteModelCardRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker Model Card.
   */
  deleteModelCard(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker model explainability job definition.
   */
  deleteModelExplainabilityJobDefinition(params: SageMaker.Types.DeleteModelExplainabilityJobDefinitionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an Amazon SageMaker model explainability job definition.
   */
  deleteModelExplainabilityJobDefinition(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
   */
  deleteModelPackage(params: SageMaker.Types.DeleteModelPackageInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a model package. A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.
   */
  deleteModelPackage(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified model group.
   */
  deleteModelPackageGroup(params: SageMaker.Types.DeleteModelPackageGroupInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified model group.
   */
  deleteModelPackageGroup(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a model group resource policy.
   */
  deleteModelPackageGroupPolicy(params: SageMaker.Types.DeleteModelPackageGroupPolicyInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a model group resource policy.
   */
  deleteModelPackageGroupPolicy(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the secified model quality monitoring job definition.
   */
  deleteModelQualityJobDefinition(params: SageMaker.Types.DeleteModelQualityJobDefinitionRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the secified model quality monitoring job definition.
   */
  deleteModelQualityJobDefinition(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule. 
   */
  deleteMonitoringSchedule(params: SageMaker.Types.DeleteMonitoringScheduleRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule. 
   */
  deleteMonitoringSchedule(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   *  Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.   When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.  
   */
  deleteNotebookInstance(params: SageMaker.Types.DeleteNotebookInstanceInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   *  Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.   When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.  
   */
  deleteNotebookInstance(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a notebook instance lifecycle configuration.
   */
  deleteNotebookInstanceLifecycleConfig(params: SageMaker.Types.DeleteNotebookInstanceLifecycleConfigInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a notebook instance lifecycle configuration.
   */
  deleteNotebookInstanceLifecycleConfig(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an optimization job.
   */
  deleteOptimizationJob(params: SageMaker.Types.DeleteOptimizationJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes an optimization job.
   */
  deleteOptimizationJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
   */
  deletePipeline(params: SageMaker.Types.DeletePipelineRequest, callback?: (err: AWSError, data: SageMaker.Types.DeletePipelineResponse) => void): Request<SageMaker.Types.DeletePipelineResponse, AWSError>;
  /**
   * Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.
   */
  deletePipeline(callback?: (err: AWSError, data: SageMaker.Types.DeletePipelineResponse) => void): Request<SageMaker.Types.DeletePipelineResponse, AWSError>;
  /**
   * Delete the specified project.
   */
  deleteProject(params: SageMaker.Types.DeleteProjectInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Delete the specified project.
   */
  deleteProject(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to delete a space.
   */
  deleteSpace(params: SageMaker.Types.DeleteSpaceRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Used to delete a space.
   */
  deleteSpace(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
   */
  deleteStudioLifecycleConfig(params: SageMaker.Types.DeleteStudioLifecycleConfigRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the Amazon SageMaker Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.
   */
  deleteStudioLifecycleConfig(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API.   When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.   When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API. 
   */
  deleteTags(params: SageMaker.Types.DeleteTagsInput, callback?: (err: AWSError, data: SageMaker.Types.DeleteTagsOutput) => void): Request<SageMaker.Types.DeleteTagsOutput, AWSError>;
  /**
   * Deletes the specified tags from an SageMaker resource. To list a resource's tags, use the ListTags API.   When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.   When you call this API to delete tags from a SageMaker Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Domain or User Profile launched before you called this API. 
   */
  deleteTags(callback?: (err: AWSError, data: SageMaker.Types.DeleteTagsOutput) => void): Request<SageMaker.Types.DeleteTagsOutput, AWSError>;
  /**
   * Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
   */
  deleteTrial(params: SageMaker.Types.DeleteTrialRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteTrialResponse) => void): Request<SageMaker.Types.DeleteTrialResponse, AWSError>;
  /**
   * Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.
   */
  deleteTrial(callback?: (err: AWSError, data: SageMaker.Types.DeleteTrialResponse) => void): Request<SageMaker.Types.DeleteTrialResponse, AWSError>;
  /**
   * Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  deleteTrialComponent(params: SageMaker.Types.DeleteTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteTrialComponentResponse) => void): Request<SageMaker.Types.DeleteTrialComponentResponse, AWSError>;
  /**
   * Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.
   */
  deleteTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.DeleteTrialComponentResponse) => void): Request<SageMaker.Types.DeleteTrialComponentResponse, AWSError>;
  /**
   * Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
   */
  deleteUserProfile(params: SageMaker.Types.DeleteUserProfileRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
   */
  deleteUserProfile(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.  If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error. 
   */
  deleteWorkforce(params: SageMaker.Types.DeleteWorkforceRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteWorkforceResponse) => void): Request<SageMaker.Types.DeleteWorkforceResponse, AWSError>;
  /**
   * Use this operation to delete a workforce. If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.  If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will receive a ResourceInUse error. 
   */
  deleteWorkforce(callback?: (err: AWSError, data: SageMaker.Types.DeleteWorkforceResponse) => void): Request<SageMaker.Types.DeleteWorkforceResponse, AWSError>;
  /**
   * Deletes an existing work team. This operation can't be undone.
   */
  deleteWorkteam(params: SageMaker.Types.DeleteWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.DeleteWorkteamResponse) => void): Request<SageMaker.Types.DeleteWorkteamResponse, AWSError>;
  /**
   * Deletes an existing work team. This operation can't be undone.
   */
  deleteWorkteam(callback?: (err: AWSError, data: SageMaker.Types.DeleteWorkteamResponse) => void): Request<SageMaker.Types.DeleteWorkteamResponse, AWSError>;
  /**
   * Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
   */
  deregisterDevices(params: SageMaker.Types.DeregisterDevicesRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.
   */
  deregisterDevices(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Describes an action.
   */
  describeAction(params: SageMaker.Types.DescribeActionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeActionResponse) => void): Request<SageMaker.Types.DescribeActionResponse, AWSError>;
  /**
   * Describes an action.
   */
  describeAction(callback?: (err: AWSError, data: SageMaker.Types.DescribeActionResponse) => void): Request<SageMaker.Types.DescribeActionResponse, AWSError>;
  /**
   * Returns a description of the specified algorithm that is in your account.
   */
  describeAlgorithm(params: SageMaker.Types.DescribeAlgorithmInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeAlgorithmOutput) => void): Request<SageMaker.Types.DescribeAlgorithmOutput, AWSError>;
  /**
   * Returns a description of the specified algorithm that is in your account.
   */
  describeAlgorithm(callback?: (err: AWSError, data: SageMaker.Types.DescribeAlgorithmOutput) => void): Request<SageMaker.Types.DescribeAlgorithmOutput, AWSError>;
  /**
   * Describes the app.
   */
  describeApp(params: SageMaker.Types.DescribeAppRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeAppResponse) => void): Request<SageMaker.Types.DescribeAppResponse, AWSError>;
  /**
   * Describes the app.
   */
  describeApp(callback?: (err: AWSError, data: SageMaker.Types.DescribeAppResponse) => void): Request<SageMaker.Types.DescribeAppResponse, AWSError>;
  /**
   * Describes an AppImageConfig.
   */
  describeAppImageConfig(params: SageMaker.Types.DescribeAppImageConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeAppImageConfigResponse) => void): Request<SageMaker.Types.DescribeAppImageConfigResponse, AWSError>;
  /**
   * Describes an AppImageConfig.
   */
  describeAppImageConfig(callback?: (err: AWSError, data: SageMaker.Types.DescribeAppImageConfigResponse) => void): Request<SageMaker.Types.DescribeAppImageConfigResponse, AWSError>;
  /**
   * Describes an artifact.
   */
  describeArtifact(params: SageMaker.Types.DescribeArtifactRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeArtifactResponse) => void): Request<SageMaker.Types.DescribeArtifactResponse, AWSError>;
  /**
   * Describes an artifact.
   */
  describeArtifact(callback?: (err: AWSError, data: SageMaker.Types.DescribeArtifactResponse) => void): Request<SageMaker.Types.DescribeArtifactResponse, AWSError>;
  /**
   * Returns information about an AutoML job created by calling CreateAutoMLJob.  AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob. 
   */
  describeAutoMLJob(params: SageMaker.Types.DescribeAutoMLJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeAutoMLJobResponse) => void): Request<SageMaker.Types.DescribeAutoMLJobResponse, AWSError>;
  /**
   * Returns information about an AutoML job created by calling CreateAutoMLJob.  AutoML jobs created by calling CreateAutoMLJobV2 cannot be described by DescribeAutoMLJob. 
   */
  describeAutoMLJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeAutoMLJobResponse) => void): Request<SageMaker.Types.DescribeAutoMLJobResponse, AWSError>;
  /**
   * Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
   */
  describeAutoMLJobV2(params: SageMaker.Types.DescribeAutoMLJobV2Request, callback?: (err: AWSError, data: SageMaker.Types.DescribeAutoMLJobV2Response) => void): Request<SageMaker.Types.DescribeAutoMLJobV2Response, AWSError>;
  /**
   * Returns information about an AutoML job created by calling CreateAutoMLJobV2 or CreateAutoMLJob.
   */
  describeAutoMLJobV2(callback?: (err: AWSError, data: SageMaker.Types.DescribeAutoMLJobV2Response) => void): Request<SageMaker.Types.DescribeAutoMLJobV2Response, AWSError>;
  /**
   * Retrieves information of a SageMaker HyperPod cluster.
   */
  describeCluster(params: SageMaker.Types.DescribeClusterRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeClusterResponse) => void): Request<SageMaker.Types.DescribeClusterResponse, AWSError>;
  /**
   * Retrieves information of a SageMaker HyperPod cluster.
   */
  describeCluster(callback?: (err: AWSError, data: SageMaker.Types.DescribeClusterResponse) => void): Request<SageMaker.Types.DescribeClusterResponse, AWSError>;
  /**
   * Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
   */
  describeClusterNode(params: SageMaker.Types.DescribeClusterNodeRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeClusterNodeResponse) => void): Request<SageMaker.Types.DescribeClusterNodeResponse, AWSError>;
  /**
   * Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.
   */
  describeClusterNode(callback?: (err: AWSError, data: SageMaker.Types.DescribeClusterNodeResponse) => void): Request<SageMaker.Types.DescribeClusterNodeResponse, AWSError>;
  /**
   * Gets details about the specified Git repository.
   */
  describeCodeRepository(params: SageMaker.Types.DescribeCodeRepositoryInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeCodeRepositoryOutput) => void): Request<SageMaker.Types.DescribeCodeRepositoryOutput, AWSError>;
  /**
   * Gets details about the specified Git repository.
   */
  describeCodeRepository(callback?: (err: AWSError, data: SageMaker.Types.DescribeCodeRepositoryOutput) => void): Request<SageMaker.Types.DescribeCodeRepositoryOutput, AWSError>;
  /**
   * Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  describeCompilationJob(params: SageMaker.Types.DescribeCompilationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeCompilationJobResponse) => void): Request<SageMaker.Types.DescribeCompilationJobResponse, AWSError>;
  /**
   * Returns information about a model compilation job. To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.
   */
  describeCompilationJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeCompilationJobResponse) => void): Request<SageMaker.Types.DescribeCompilationJobResponse, AWSError>;
  /**
   * Describes a context.
   */
  describeContext(params: SageMaker.Types.DescribeContextRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeContextResponse) => void): Request<SageMaker.Types.DescribeContextResponse, AWSError>;
  /**
   * Describes a context.
   */
  describeContext(callback?: (err: AWSError, data: SageMaker.Types.DescribeContextResponse) => void): Request<SageMaker.Types.DescribeContextResponse, AWSError>;
  /**
   * Gets the details of a data quality monitoring job definition.
   */
  describeDataQualityJobDefinition(params: SageMaker.Types.DescribeDataQualityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Gets the details of a data quality monitoring job definition.
   */
  describeDataQualityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.DescribeDataQualityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeDataQualityJobDefinitionResponse, AWSError>;
  /**
   * Describes the device.
   */
  describeDevice(params: SageMaker.Types.DescribeDeviceRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeDeviceResponse) => void): Request<SageMaker.Types.DescribeDeviceResponse, AWSError>;
  /**
   * Describes the device.
   */
  describeDevice(callback?: (err: AWSError, data: SageMaker.Types.DescribeDeviceResponse) => void): Request<SageMaker.Types.DescribeDeviceResponse, AWSError>;
  /**
   * A description of the fleet the device belongs to.
   */
  describeDeviceFleet(params: SageMaker.Types.DescribeDeviceFleetRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeDeviceFleetResponse) => void): Request<SageMaker.Types.DescribeDeviceFleetResponse, AWSError>;
  /**
   * A description of the fleet the device belongs to.
   */
  describeDeviceFleet(callback?: (err: AWSError, data: SageMaker.Types.DescribeDeviceFleetResponse) => void): Request<SageMaker.Types.DescribeDeviceFleetResponse, AWSError>;
  /**
   * The description of the domain.
   */
  describeDomain(params: SageMaker.Types.DescribeDomainRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeDomainResponse) => void): Request<SageMaker.Types.DescribeDomainResponse, AWSError>;
  /**
   * The description of the domain.
   */
  describeDomain(callback?: (err: AWSError, data: SageMaker.Types.DescribeDomainResponse) => void): Request<SageMaker.Types.DescribeDomainResponse, AWSError>;
  /**
   * Describes an edge deployment plan with deployment status per stage.
   */
  describeEdgeDeploymentPlan(params: SageMaker.Types.DescribeEdgeDeploymentPlanRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeEdgeDeploymentPlanResponse) => void): Request<SageMaker.Types.DescribeEdgeDeploymentPlanResponse, AWSError>;
  /**
   * Describes an edge deployment plan with deployment status per stage.
   */
  describeEdgeDeploymentPlan(callback?: (err: AWSError, data: SageMaker.Types.DescribeEdgeDeploymentPlanResponse) => void): Request<SageMaker.Types.DescribeEdgeDeploymentPlanResponse, AWSError>;
  /**
   * A description of edge packaging jobs.
   */
  describeEdgePackagingJob(params: SageMaker.Types.DescribeEdgePackagingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeEdgePackagingJobResponse) => void): Request<SageMaker.Types.DescribeEdgePackagingJobResponse, AWSError>;
  /**
   * A description of edge packaging jobs.
   */
  describeEdgePackagingJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeEdgePackagingJobResponse) => void): Request<SageMaker.Types.DescribeEdgePackagingJobResponse, AWSError>;
  /**
   * Returns the description of an endpoint.
   */
  describeEndpoint(params: SageMaker.Types.DescribeEndpointInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Returns the description of an endpoint.
   */
  describeEndpoint(callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
   */
  describeEndpointConfig(params: SageMaker.Types.DescribeEndpointConfigInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointConfigOutput) => void): Request<SageMaker.Types.DescribeEndpointConfigOutput, AWSError>;
  /**
   * Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
   */
  describeEndpointConfig(callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointConfigOutput) => void): Request<SageMaker.Types.DescribeEndpointConfigOutput, AWSError>;
  /**
   * Provides a list of an experiment's properties.
   */
  describeExperiment(params: SageMaker.Types.DescribeExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeExperimentResponse) => void): Request<SageMaker.Types.DescribeExperimentResponse, AWSError>;
  /**
   * Provides a list of an experiment's properties.
   */
  describeExperiment(callback?: (err: AWSError, data: SageMaker.Types.DescribeExperimentResponse) => void): Request<SageMaker.Types.DescribeExperimentResponse, AWSError>;
  /**
   * Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
   */
  describeFeatureGroup(params: SageMaker.Types.DescribeFeatureGroupRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeFeatureGroupResponse) => void): Request<SageMaker.Types.DescribeFeatureGroupResponse, AWSError>;
  /**
   * Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.
   */
  describeFeatureGroup(callback?: (err: AWSError, data: SageMaker.Types.DescribeFeatureGroupResponse) => void): Request<SageMaker.Types.DescribeFeatureGroupResponse, AWSError>;
  /**
   * Shows the metadata for a feature within a feature group.
   */
  describeFeatureMetadata(params: SageMaker.Types.DescribeFeatureMetadataRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeFeatureMetadataResponse) => void): Request<SageMaker.Types.DescribeFeatureMetadataResponse, AWSError>;
  /**
   * Shows the metadata for a feature within a feature group.
   */
  describeFeatureMetadata(callback?: (err: AWSError, data: SageMaker.Types.DescribeFeatureMetadataResponse) => void): Request<SageMaker.Types.DescribeFeatureMetadataResponse, AWSError>;
  /**
   * Returns information about the specified flow definition.
   */
  describeFlowDefinition(params: SageMaker.Types.DescribeFlowDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeFlowDefinitionResponse) => void): Request<SageMaker.Types.DescribeFlowDefinitionResponse, AWSError>;
  /**
   * Returns information about the specified flow definition.
   */
  describeFlowDefinition(callback?: (err: AWSError, data: SageMaker.Types.DescribeFlowDefinitionResponse) => void): Request<SageMaker.Types.DescribeFlowDefinitionResponse, AWSError>;
  /**
   * Describes a hub.
   */
  describeHub(params: SageMaker.Types.DescribeHubRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeHubResponse) => void): Request<SageMaker.Types.DescribeHubResponse, AWSError>;
  /**
   * Describes a hub.
   */
  describeHub(callback?: (err: AWSError, data: SageMaker.Types.DescribeHubResponse) => void): Request<SageMaker.Types.DescribeHubResponse, AWSError>;
  /**
   * Describe the content of a hub.
   */
  describeHubContent(params: SageMaker.Types.DescribeHubContentRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeHubContentResponse) => void): Request<SageMaker.Types.DescribeHubContentResponse, AWSError>;
  /**
   * Describe the content of a hub.
   */
  describeHubContent(callback?: (err: AWSError, data: SageMaker.Types.DescribeHubContentResponse) => void): Request<SageMaker.Types.DescribeHubContentResponse, AWSError>;
  /**
   * Returns information about the requested human task user interface (worker task template).
   */
  describeHumanTaskUi(params: SageMaker.Types.DescribeHumanTaskUiRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeHumanTaskUiResponse) => void): Request<SageMaker.Types.DescribeHumanTaskUiResponse, AWSError>;
  /**
   * Returns information about the requested human task user interface (worker task template).
   */
  describeHumanTaskUi(callback?: (err: AWSError, data: SageMaker.Types.DescribeHumanTaskUiResponse) => void): Request<SageMaker.Types.DescribeHumanTaskUiResponse, AWSError>;
  /**
   * Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
   */
  describeHyperParameterTuningJob(params: SageMaker.Types.DescribeHyperParameterTuningJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.DescribeHyperParameterTuningJobResponse, AWSError>;
  /**
   * Returns a description of a hyperparameter tuning job, depending on the fields selected. These fields can include the name, Amazon Resource Name (ARN), job status of your tuning job and more.
   */
  describeHyperParameterTuningJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.DescribeHyperParameterTuningJobResponse, AWSError>;
  /**
   * Describes a SageMaker image.
   */
  describeImage(params: SageMaker.Types.DescribeImageRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Describes a SageMaker image.
   */
  describeImage(callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Describes a version of a SageMaker image.
   */
  describeImageVersion(params: SageMaker.Types.DescribeImageVersionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
  /**
   * Describes a version of a SageMaker image.
   */
  describeImageVersion(callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
  /**
   * Returns information about an inference component.
   */
  describeInferenceComponent(params: SageMaker.Types.DescribeInferenceComponentInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceComponentOutput) => void): Request<SageMaker.Types.DescribeInferenceComponentOutput, AWSError>;
  /**
   * Returns information about an inference component.
   */
  describeInferenceComponent(callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceComponentOutput) => void): Request<SageMaker.Types.DescribeInferenceComponentOutput, AWSError>;
  /**
   * Returns details about an inference experiment.
   */
  describeInferenceExperiment(params: SageMaker.Types.DescribeInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceExperimentResponse) => void): Request<SageMaker.Types.DescribeInferenceExperimentResponse, AWSError>;
  /**
   * Returns details about an inference experiment.
   */
  describeInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceExperimentResponse) => void): Request<SageMaker.Types.DescribeInferenceExperimentResponse, AWSError>;
  /**
   * Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
   */
  describeInferenceRecommendationsJob(params: SageMaker.Types.DescribeInferenceRecommendationsJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceRecommendationsJobResponse) => void): Request<SageMaker.Types.DescribeInferenceRecommendationsJobResponse, AWSError>;
  /**
   * Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.
   */
  describeInferenceRecommendationsJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeInferenceRecommendationsJobResponse) => void): Request<SageMaker.Types.DescribeInferenceRecommendationsJobResponse, AWSError>;
  /**
   * Gets information about a labeling job.
   */
  describeLabelingJob(params: SageMaker.Types.DescribeLabelingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeLabelingJobResponse) => void): Request<SageMaker.Types.DescribeLabelingJobResponse, AWSError>;
  /**
   * Gets information about a labeling job.
   */
  describeLabelingJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeLabelingJobResponse) => void): Request<SageMaker.Types.DescribeLabelingJobResponse, AWSError>;
  /**
   * Provides a list of properties for the requested lineage group. For more information, see  Cross-Account Lineage Tracking  in the Amazon SageMaker Developer Guide.
   */
  describeLineageGroup(params: SageMaker.Types.DescribeLineageGroupRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeLineageGroupResponse) => void): Request<SageMaker.Types.DescribeLineageGroupResponse, AWSError>;
  /**
   * Provides a list of properties for the requested lineage group. For more information, see  Cross-Account Lineage Tracking  in the Amazon SageMaker Developer Guide.
   */
  describeLineageGroup(callback?: (err: AWSError, data: SageMaker.Types.DescribeLineageGroupResponse) => void): Request<SageMaker.Types.DescribeLineageGroupResponse, AWSError>;
  /**
   * Returns information about an MLflow Tracking Server.
   */
  describeMlflowTrackingServer(params: SageMaker.Types.DescribeMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeMlflowTrackingServerResponse) => void): Request<SageMaker.Types.DescribeMlflowTrackingServerResponse, AWSError>;
  /**
   * Returns information about an MLflow Tracking Server.
   */
  describeMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.DescribeMlflowTrackingServerResponse) => void): Request<SageMaker.Types.DescribeMlflowTrackingServerResponse, AWSError>;
  /**
   * Describes a model that you created using the CreateModel API.
   */
  describeModel(params: SageMaker.Types.DescribeModelInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelOutput) => void): Request<SageMaker.Types.DescribeModelOutput, AWSError>;
  /**
   * Describes a model that you created using the CreateModel API.
   */
  describeModel(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelOutput) => void): Request<SageMaker.Types.DescribeModelOutput, AWSError>;
  /**
   * Returns a description of a model bias job definition.
   */
  describeModelBiasJobDefinition(params: SageMaker.Types.DescribeModelBiasJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelBiasJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelBiasJobDefinitionResponse, AWSError>;
  /**
   * Returns a description of a model bias job definition.
   */
  describeModelBiasJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelBiasJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelBiasJobDefinitionResponse, AWSError>;
  /**
   * Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
   */
  describeModelCard(params: SageMaker.Types.DescribeModelCardRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelCardResponse) => void): Request<SageMaker.Types.DescribeModelCardResponse, AWSError>;
  /**
   * Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.
   */
  describeModelCard(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelCardResponse) => void): Request<SageMaker.Types.DescribeModelCardResponse, AWSError>;
  /**
   * Describes an Amazon SageMaker Model Card export job.
   */
  describeModelCardExportJob(params: SageMaker.Types.DescribeModelCardExportJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelCardExportJobResponse) => void): Request<SageMaker.Types.DescribeModelCardExportJobResponse, AWSError>;
  /**
   * Describes an Amazon SageMaker Model Card export job.
   */
  describeModelCardExportJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelCardExportJobResponse) => void): Request<SageMaker.Types.DescribeModelCardExportJobResponse, AWSError>;
  /**
   * Returns a description of a model explainability job definition.
   */
  describeModelExplainabilityJobDefinition(params: SageMaker.Types.DescribeModelExplainabilityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelExplainabilityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelExplainabilityJobDefinitionResponse, AWSError>;
  /**
   * Returns a description of a model explainability job definition.
   */
  describeModelExplainabilityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelExplainabilityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelExplainabilityJobDefinitionResponse, AWSError>;
  /**
   * Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.  If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.  To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
   */
  describeModelPackage(params: SageMaker.Types.DescribeModelPackageInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelPackageOutput) => void): Request<SageMaker.Types.DescribeModelPackageOutput, AWSError>;
  /**
   * Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.  If you provided a KMS Key ID when you created your model package, you will see the KMS Decrypt API call in your CloudTrail logs when you use this API.  To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.
   */
  describeModelPackage(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelPackageOutput) => void): Request<SageMaker.Types.DescribeModelPackageOutput, AWSError>;
  /**
   * Gets a description for the specified model group.
   */
  describeModelPackageGroup(params: SageMaker.Types.DescribeModelPackageGroupInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelPackageGroupOutput) => void): Request<SageMaker.Types.DescribeModelPackageGroupOutput, AWSError>;
  /**
   * Gets a description for the specified model group.
   */
  describeModelPackageGroup(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelPackageGroupOutput) => void): Request<SageMaker.Types.DescribeModelPackageGroupOutput, AWSError>;
  /**
   * Returns a description of a model quality job definition.
   */
  describeModelQualityJobDefinition(params: SageMaker.Types.DescribeModelQualityJobDefinitionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeModelQualityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelQualityJobDefinitionResponse, AWSError>;
  /**
   * Returns a description of a model quality job definition.
   */
  describeModelQualityJobDefinition(callback?: (err: AWSError, data: SageMaker.Types.DescribeModelQualityJobDefinitionResponse) => void): Request<SageMaker.Types.DescribeModelQualityJobDefinitionResponse, AWSError>;
  /**
   * Describes the schedule for a monitoring job.
   */
  describeMonitoringSchedule(params: SageMaker.Types.DescribeMonitoringScheduleRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeMonitoringScheduleResponse) => void): Request<SageMaker.Types.DescribeMonitoringScheduleResponse, AWSError>;
  /**
   * Describes the schedule for a monitoring job.
   */
  describeMonitoringSchedule(callback?: (err: AWSError, data: SageMaker.Types.DescribeMonitoringScheduleResponse) => void): Request<SageMaker.Types.DescribeMonitoringScheduleResponse, AWSError>;
  /**
   * Returns information about a notebook instance.
   */
  describeNotebookInstance(params: SageMaker.Types.DescribeNotebookInstanceInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Returns information about a notebook instance.
   */
  describeNotebookInstance(callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
   */
  describeNotebookInstanceLifecycleConfig(params: SageMaker.Types.DescribeNotebookInstanceLifecycleConfigInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Returns a description of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
   */
  describeNotebookInstanceLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Provides the properties of the specified optimization job.
   */
  describeOptimizationJob(params: SageMaker.Types.DescribeOptimizationJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeOptimizationJobResponse) => void): Request<SageMaker.Types.DescribeOptimizationJobResponse, AWSError>;
  /**
   * Provides the properties of the specified optimization job.
   */
  describeOptimizationJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeOptimizationJobResponse) => void): Request<SageMaker.Types.DescribeOptimizationJobResponse, AWSError>;
  /**
   * Describes the details of a pipeline.
   */
  describePipeline(params: SageMaker.Types.DescribePipelineRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineResponse) => void): Request<SageMaker.Types.DescribePipelineResponse, AWSError>;
  /**
   * Describes the details of a pipeline.
   */
  describePipeline(callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineResponse) => void): Request<SageMaker.Types.DescribePipelineResponse, AWSError>;
  /**
   * Describes the details of an execution's pipeline definition.
   */
  describePipelineDefinitionForExecution(params: SageMaker.Types.DescribePipelineDefinitionForExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineDefinitionForExecutionResponse) => void): Request<SageMaker.Types.DescribePipelineDefinitionForExecutionResponse, AWSError>;
  /**
   * Describes the details of an execution's pipeline definition.
   */
  describePipelineDefinitionForExecution(callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineDefinitionForExecutionResponse) => void): Request<SageMaker.Types.DescribePipelineDefinitionForExecutionResponse, AWSError>;
  /**
   * Describes the details of a pipeline execution.
   */
  describePipelineExecution(params: SageMaker.Types.DescribePipelineExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineExecutionResponse) => void): Request<SageMaker.Types.DescribePipelineExecutionResponse, AWSError>;
  /**
   * Describes the details of a pipeline execution.
   */
  describePipelineExecution(callback?: (err: AWSError, data: SageMaker.Types.DescribePipelineExecutionResponse) => void): Request<SageMaker.Types.DescribePipelineExecutionResponse, AWSError>;
  /**
   * Returns a description of a processing job.
   */
  describeProcessingJob(params: SageMaker.Types.DescribeProcessingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeProcessingJobResponse) => void): Request<SageMaker.Types.DescribeProcessingJobResponse, AWSError>;
  /**
   * Returns a description of a processing job.
   */
  describeProcessingJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeProcessingJobResponse) => void): Request<SageMaker.Types.DescribeProcessingJobResponse, AWSError>;
  /**
   * Describes the details of a project.
   */
  describeProject(params: SageMaker.Types.DescribeProjectInput, callback?: (err: AWSError, data: SageMaker.Types.DescribeProjectOutput) => void): Request<SageMaker.Types.DescribeProjectOutput, AWSError>;
  /**
   * Describes the details of a project.
   */
  describeProject(callback?: (err: AWSError, data: SageMaker.Types.DescribeProjectOutput) => void): Request<SageMaker.Types.DescribeProjectOutput, AWSError>;
  /**
   * Describes the space.
   */
  describeSpace(params: SageMaker.Types.DescribeSpaceRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeSpaceResponse) => void): Request<SageMaker.Types.DescribeSpaceResponse, AWSError>;
  /**
   * Describes the space.
   */
  describeSpace(callback?: (err: AWSError, data: SageMaker.Types.DescribeSpaceResponse) => void): Request<SageMaker.Types.DescribeSpaceResponse, AWSError>;
  /**
   * Describes the Amazon SageMaker Studio Lifecycle Configuration.
   */
  describeStudioLifecycleConfig(params: SageMaker.Types.DescribeStudioLifecycleConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeStudioLifecycleConfigResponse) => void): Request<SageMaker.Types.DescribeStudioLifecycleConfigResponse, AWSError>;
  /**
   * Describes the Amazon SageMaker Studio Lifecycle Configuration.
   */
  describeStudioLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.DescribeStudioLifecycleConfigResponse) => void): Request<SageMaker.Types.DescribeStudioLifecycleConfigResponse, AWSError>;
  /**
   * Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
   */
  describeSubscribedWorkteam(params: SageMaker.Types.DescribeSubscribedWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeSubscribedWorkteamResponse) => void): Request<SageMaker.Types.DescribeSubscribedWorkteamResponse, AWSError>;
  /**
   * Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.
   */
  describeSubscribedWorkteam(callback?: (err: AWSError, data: SageMaker.Types.DescribeSubscribedWorkteamResponse) => void): Request<SageMaker.Types.DescribeSubscribedWorkteamResponse, AWSError>;
  /**
   * Returns information about a training job.  Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
   */
  describeTrainingJob(params: SageMaker.Types.DescribeTrainingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeTrainingJobResponse) => void): Request<SageMaker.Types.DescribeTrainingJobResponse, AWSError>;
  /**
   * Returns information about a training job.  Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.
   */
  describeTrainingJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeTrainingJobResponse) => void): Request<SageMaker.Types.DescribeTrainingJobResponse, AWSError>;
  /**
   * Returns information about a transform job.
   */
  describeTransformJob(params: SageMaker.Types.DescribeTransformJobRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeTransformJobResponse) => void): Request<SageMaker.Types.DescribeTransformJobResponse, AWSError>;
  /**
   * Returns information about a transform job.
   */
  describeTransformJob(callback?: (err: AWSError, data: SageMaker.Types.DescribeTransformJobResponse) => void): Request<SageMaker.Types.DescribeTransformJobResponse, AWSError>;
  /**
   * Provides a list of a trial's properties.
   */
  describeTrial(params: SageMaker.Types.DescribeTrialRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeTrialResponse) => void): Request<SageMaker.Types.DescribeTrialResponse, AWSError>;
  /**
   * Provides a list of a trial's properties.
   */
  describeTrial(callback?: (err: AWSError, data: SageMaker.Types.DescribeTrialResponse) => void): Request<SageMaker.Types.DescribeTrialResponse, AWSError>;
  /**
   * Provides a list of a trials component's properties.
   */
  describeTrialComponent(params: SageMaker.Types.DescribeTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeTrialComponentResponse) => void): Request<SageMaker.Types.DescribeTrialComponentResponse, AWSError>;
  /**
   * Provides a list of a trials component's properties.
   */
  describeTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.DescribeTrialComponentResponse) => void): Request<SageMaker.Types.DescribeTrialComponentResponse, AWSError>;
  /**
   * Describes a user profile. For more information, see CreateUserProfile.
   */
  describeUserProfile(params: SageMaker.Types.DescribeUserProfileRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeUserProfileResponse) => void): Request<SageMaker.Types.DescribeUserProfileResponse, AWSError>;
  /**
   * Describes a user profile. For more information, see CreateUserProfile.
   */
  describeUserProfile(callback?: (err: AWSError, data: SageMaker.Types.DescribeUserProfileResponse) => void): Request<SageMaker.Types.DescribeUserProfileResponse, AWSError>;
  /**
   * Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.   This operation applies only to private workforces. 
   */
  describeWorkforce(params: SageMaker.Types.DescribeWorkforceRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeWorkforceResponse) => void): Request<SageMaker.Types.DescribeWorkforceResponse, AWSError>;
  /**
   * Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs). Allowable IP address ranges are the IP addresses that workers can use to access tasks.   This operation applies only to private workforces. 
   */
  describeWorkforce(callback?: (err: AWSError, data: SageMaker.Types.DescribeWorkforceResponse) => void): Request<SageMaker.Types.DescribeWorkforceResponse, AWSError>;
  /**
   * Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
   */
  describeWorkteam(params: SageMaker.Types.DescribeWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.DescribeWorkteamResponse) => void): Request<SageMaker.Types.DescribeWorkteamResponse, AWSError>;
  /**
   * Gets information about a specific work team. You can see information such as the creation date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
   */
  describeWorkteam(callback?: (err: AWSError, data: SageMaker.Types.DescribeWorkteamResponse) => void): Request<SageMaker.Types.DescribeWorkteamResponse, AWSError>;
  /**
   * Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  disableSagemakerServicecatalogPortfolio(params: SageMaker.Types.DisableSagemakerServicecatalogPortfolioInput, callback?: (err: AWSError, data: SageMaker.Types.DisableSagemakerServicecatalogPortfolioOutput) => void): Request<SageMaker.Types.DisableSagemakerServicecatalogPortfolioOutput, AWSError>;
  /**
   * Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  disableSagemakerServicecatalogPortfolio(callback?: (err: AWSError, data: SageMaker.Types.DisableSagemakerServicecatalogPortfolioOutput) => void): Request<SageMaker.Types.DisableSagemakerServicecatalogPortfolioOutput, AWSError>;
  /**
   * Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
   */
  disassociateTrialComponent(params: SageMaker.Types.DisassociateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.DisassociateTrialComponentResponse) => void): Request<SageMaker.Types.DisassociateTrialComponentResponse, AWSError>;
  /**
   * Disassociates a trial component from a trial. This doesn't effect other trials the component is associated with. Before you can delete a component, you must disassociate the component from all trials it is associated with. To associate a trial component with a trial, call the AssociateTrialComponent API. To get a list of the trials a component is associated with, use the Search API. Specify ExperimentTrialComponent for the Resource parameter. The list appears in the response under Results.TrialComponent.Parents.
   */
  disassociateTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.DisassociateTrialComponentResponse) => void): Request<SageMaker.Types.DisassociateTrialComponentResponse, AWSError>;
  /**
   * Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  enableSagemakerServicecatalogPortfolio(params: SageMaker.Types.EnableSagemakerServicecatalogPortfolioInput, callback?: (err: AWSError, data: SageMaker.Types.EnableSagemakerServicecatalogPortfolioOutput) => void): Request<SageMaker.Types.EnableSagemakerServicecatalogPortfolioOutput, AWSError>;
  /**
   * Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  enableSagemakerServicecatalogPortfolio(callback?: (err: AWSError, data: SageMaker.Types.EnableSagemakerServicecatalogPortfolioOutput) => void): Request<SageMaker.Types.EnableSagemakerServicecatalogPortfolioOutput, AWSError>;
  /**
   * Describes a fleet.
   */
  getDeviceFleetReport(params: SageMaker.Types.GetDeviceFleetReportRequest, callback?: (err: AWSError, data: SageMaker.Types.GetDeviceFleetReportResponse) => void): Request<SageMaker.Types.GetDeviceFleetReportResponse, AWSError>;
  /**
   * Describes a fleet.
   */
  getDeviceFleetReport(callback?: (err: AWSError, data: SageMaker.Types.GetDeviceFleetReportResponse) => void): Request<SageMaker.Types.GetDeviceFleetReportResponse, AWSError>;
  /**
   * The resource policy for the lineage group.
   */
  getLineageGroupPolicy(params: SageMaker.Types.GetLineageGroupPolicyRequest, callback?: (err: AWSError, data: SageMaker.Types.GetLineageGroupPolicyResponse) => void): Request<SageMaker.Types.GetLineageGroupPolicyResponse, AWSError>;
  /**
   * The resource policy for the lineage group.
   */
  getLineageGroupPolicy(callback?: (err: AWSError, data: SageMaker.Types.GetLineageGroupPolicyResponse) => void): Request<SageMaker.Types.GetLineageGroupPolicyResponse, AWSError>;
  /**
   * Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
   */
  getModelPackageGroupPolicy(params: SageMaker.Types.GetModelPackageGroupPolicyInput, callback?: (err: AWSError, data: SageMaker.Types.GetModelPackageGroupPolicyOutput) => void): Request<SageMaker.Types.GetModelPackageGroupPolicyOutput, AWSError>;
  /**
   * Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
   */
  getModelPackageGroupPolicy(callback?: (err: AWSError, data: SageMaker.Types.GetModelPackageGroupPolicyOutput) => void): Request<SageMaker.Types.GetModelPackageGroupPolicyOutput, AWSError>;
  /**
   * Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  getSagemakerServicecatalogPortfolioStatus(params: SageMaker.Types.GetSagemakerServicecatalogPortfolioStatusInput, callback?: (err: AWSError, data: SageMaker.Types.GetSagemakerServicecatalogPortfolioStatusOutput) => void): Request<SageMaker.Types.GetSagemakerServicecatalogPortfolioStatusOutput, AWSError>;
  /**
   * Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.
   */
  getSagemakerServicecatalogPortfolioStatus(callback?: (err: AWSError, data: SageMaker.Types.GetSagemakerServicecatalogPortfolioStatusOutput) => void): Request<SageMaker.Types.GetSagemakerServicecatalogPortfolioStatusOutput, AWSError>;
  /**
   * Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
   */
  getScalingConfigurationRecommendation(params: SageMaker.Types.GetScalingConfigurationRecommendationRequest, callback?: (err: AWSError, data: SageMaker.Types.GetScalingConfigurationRecommendationResponse) => void): Request<SageMaker.Types.GetScalingConfigurationRecommendationResponse, AWSError>;
  /**
   * Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job. Returns recommendations for autoscaling policies that you can apply to your SageMaker endpoint.
   */
  getScalingConfigurationRecommendation(callback?: (err: AWSError, data: SageMaker.Types.GetScalingConfigurationRecommendationResponse) => void): Request<SageMaker.Types.GetScalingConfigurationRecommendationResponse, AWSError>;
  /**
   * An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
   */
  getSearchSuggestions(params: SageMaker.Types.GetSearchSuggestionsRequest, callback?: (err: AWSError, data: SageMaker.Types.GetSearchSuggestionsResponse) => void): Request<SageMaker.Types.GetSearchSuggestionsResponse, AWSError>;
  /**
   * An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
   */
  getSearchSuggestions(callback?: (err: AWSError, data: SageMaker.Types.GetSearchSuggestionsResponse) => void): Request<SageMaker.Types.GetSearchSuggestionsResponse, AWSError>;
  /**
   * Import hub content.
   */
  importHubContent(params: SageMaker.Types.ImportHubContentRequest, callback?: (err: AWSError, data: SageMaker.Types.ImportHubContentResponse) => void): Request<SageMaker.Types.ImportHubContentResponse, AWSError>;
  /**
   * Import hub content.
   */
  importHubContent(callback?: (err: AWSError, data: SageMaker.Types.ImportHubContentResponse) => void): Request<SageMaker.Types.ImportHubContentResponse, AWSError>;
  /**
   * Lists the actions in your account and their properties.
   */
  listActions(params: SageMaker.Types.ListActionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListActionsResponse) => void): Request<SageMaker.Types.ListActionsResponse, AWSError>;
  /**
   * Lists the actions in your account and their properties.
   */
  listActions(callback?: (err: AWSError, data: SageMaker.Types.ListActionsResponse) => void): Request<SageMaker.Types.ListActionsResponse, AWSError>;
  /**
   * Lists the machine learning algorithms that have been created.
   */
  listAlgorithms(params: SageMaker.Types.ListAlgorithmsInput, callback?: (err: AWSError, data: SageMaker.Types.ListAlgorithmsOutput) => void): Request<SageMaker.Types.ListAlgorithmsOutput, AWSError>;
  /**
   * Lists the machine learning algorithms that have been created.
   */
  listAlgorithms(callback?: (err: AWSError, data: SageMaker.Types.ListAlgorithmsOutput) => void): Request<SageMaker.Types.ListAlgorithmsOutput, AWSError>;
  /**
   * Lists the aliases of a specified image or image version.
   */
  listAliases(params: SageMaker.Types.ListAliasesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListAliasesResponse) => void): Request<SageMaker.Types.ListAliasesResponse, AWSError>;
  /**
   * Lists the aliases of a specified image or image version.
   */
  listAliases(callback?: (err: AWSError, data: SageMaker.Types.ListAliasesResponse) => void): Request<SageMaker.Types.ListAliasesResponse, AWSError>;
  /**
   * Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
   */
  listAppImageConfigs(params: SageMaker.Types.ListAppImageConfigsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListAppImageConfigsResponse) => void): Request<SageMaker.Types.ListAppImageConfigsResponse, AWSError>;
  /**
   * Lists the AppImageConfigs in your account and their properties. The list can be filtered by creation time or modified time, and whether the AppImageConfig name contains a specified string.
   */
  listAppImageConfigs(callback?: (err: AWSError, data: SageMaker.Types.ListAppImageConfigsResponse) => void): Request<SageMaker.Types.ListAppImageConfigsResponse, AWSError>;
  /**
   * Lists apps.
   */
  listApps(params: SageMaker.Types.ListAppsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListAppsResponse) => void): Request<SageMaker.Types.ListAppsResponse, AWSError>;
  /**
   * Lists apps.
   */
  listApps(callback?: (err: AWSError, data: SageMaker.Types.ListAppsResponse) => void): Request<SageMaker.Types.ListAppsResponse, AWSError>;
  /**
   * Lists the artifacts in your account and their properties.
   */
  listArtifacts(params: SageMaker.Types.ListArtifactsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListArtifactsResponse) => void): Request<SageMaker.Types.ListArtifactsResponse, AWSError>;
  /**
   * Lists the artifacts in your account and their properties.
   */
  listArtifacts(callback?: (err: AWSError, data: SageMaker.Types.ListArtifactsResponse) => void): Request<SageMaker.Types.ListArtifactsResponse, AWSError>;
  /**
   * Lists the associations in your account and their properties.
   */
  listAssociations(params: SageMaker.Types.ListAssociationsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListAssociationsResponse) => void): Request<SageMaker.Types.ListAssociationsResponse, AWSError>;
  /**
   * Lists the associations in your account and their properties.
   */
  listAssociations(callback?: (err: AWSError, data: SageMaker.Types.ListAssociationsResponse) => void): Request<SageMaker.Types.ListAssociationsResponse, AWSError>;
  /**
   * Request a list of jobs.
   */
  listAutoMLJobs(params: SageMaker.Types.ListAutoMLJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListAutoMLJobsResponse) => void): Request<SageMaker.Types.ListAutoMLJobsResponse, AWSError>;
  /**
   * Request a list of jobs.
   */
  listAutoMLJobs(callback?: (err: AWSError, data: SageMaker.Types.ListAutoMLJobsResponse) => void): Request<SageMaker.Types.ListAutoMLJobsResponse, AWSError>;
  /**
   * List the candidates created for the job.
   */
  listCandidatesForAutoMLJob(params: SageMaker.Types.ListCandidatesForAutoMLJobRequest, callback?: (err: AWSError, data: SageMaker.Types.ListCandidatesForAutoMLJobResponse) => void): Request<SageMaker.Types.ListCandidatesForAutoMLJobResponse, AWSError>;
  /**
   * List the candidates created for the job.
   */
  listCandidatesForAutoMLJob(callback?: (err: AWSError, data: SageMaker.Types.ListCandidatesForAutoMLJobResponse) => void): Request<SageMaker.Types.ListCandidatesForAutoMLJobResponse, AWSError>;
  /**
   * Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
   */
  listClusterNodes(params: SageMaker.Types.ListClusterNodesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListClusterNodesResponse) => void): Request<SageMaker.Types.ListClusterNodesResponse, AWSError>;
  /**
   * Retrieves the list of instances (also called nodes interchangeably) in a SageMaker HyperPod cluster.
   */
  listClusterNodes(callback?: (err: AWSError, data: SageMaker.Types.ListClusterNodesResponse) => void): Request<SageMaker.Types.ListClusterNodesResponse, AWSError>;
  /**
   * Retrieves the list of SageMaker HyperPod clusters.
   */
  listClusters(params: SageMaker.Types.ListClustersRequest, callback?: (err: AWSError, data: SageMaker.Types.ListClustersResponse) => void): Request<SageMaker.Types.ListClustersResponse, AWSError>;
  /**
   * Retrieves the list of SageMaker HyperPod clusters.
   */
  listClusters(callback?: (err: AWSError, data: SageMaker.Types.ListClustersResponse) => void): Request<SageMaker.Types.ListClustersResponse, AWSError>;
  /**
   * Gets a list of the Git repositories in your account.
   */
  listCodeRepositories(params: SageMaker.Types.ListCodeRepositoriesInput, callback?: (err: AWSError, data: SageMaker.Types.ListCodeRepositoriesOutput) => void): Request<SageMaker.Types.ListCodeRepositoriesOutput, AWSError>;
  /**
   * Gets a list of the Git repositories in your account.
   */
  listCodeRepositories(callback?: (err: AWSError, data: SageMaker.Types.ListCodeRepositoriesOutput) => void): Request<SageMaker.Types.ListCodeRepositoriesOutput, AWSError>;
  /**
   * Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
   */
  listCompilationJobs(params: SageMaker.Types.ListCompilationJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListCompilationJobsResponse) => void): Request<SageMaker.Types.ListCompilationJobsResponse, AWSError>;
  /**
   * Lists model compilation jobs that satisfy various filters. To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.
   */
  listCompilationJobs(callback?: (err: AWSError, data: SageMaker.Types.ListCompilationJobsResponse) => void): Request<SageMaker.Types.ListCompilationJobsResponse, AWSError>;
  /**
   * Lists the contexts in your account and their properties.
   */
  listContexts(params: SageMaker.Types.ListContextsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListContextsResponse) => void): Request<SageMaker.Types.ListContextsResponse, AWSError>;
  /**
   * Lists the contexts in your account and their properties.
   */
  listContexts(callback?: (err: AWSError, data: SageMaker.Types.ListContextsResponse) => void): Request<SageMaker.Types.ListContextsResponse, AWSError>;
  /**
   * Lists the data quality job definitions in your account.
   */
  listDataQualityJobDefinitions(params: SageMaker.Types.ListDataQualityJobDefinitionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListDataQualityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListDataQualityJobDefinitionsResponse, AWSError>;
  /**
   * Lists the data quality job definitions in your account.
   */
  listDataQualityJobDefinitions(callback?: (err: AWSError, data: SageMaker.Types.ListDataQualityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListDataQualityJobDefinitionsResponse, AWSError>;
  /**
   * Returns a list of devices in the fleet.
   */
  listDeviceFleets(params: SageMaker.Types.ListDeviceFleetsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListDeviceFleetsResponse) => void): Request<SageMaker.Types.ListDeviceFleetsResponse, AWSError>;
  /**
   * Returns a list of devices in the fleet.
   */
  listDeviceFleets(callback?: (err: AWSError, data: SageMaker.Types.ListDeviceFleetsResponse) => void): Request<SageMaker.Types.ListDeviceFleetsResponse, AWSError>;
  /**
   * A list of devices.
   */
  listDevices(params: SageMaker.Types.ListDevicesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListDevicesResponse) => void): Request<SageMaker.Types.ListDevicesResponse, AWSError>;
  /**
   * A list of devices.
   */
  listDevices(callback?: (err: AWSError, data: SageMaker.Types.ListDevicesResponse) => void): Request<SageMaker.Types.ListDevicesResponse, AWSError>;
  /**
   * Lists the domains.
   */
  listDomains(params: SageMaker.Types.ListDomainsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListDomainsResponse) => void): Request<SageMaker.Types.ListDomainsResponse, AWSError>;
  /**
   * Lists the domains.
   */
  listDomains(callback?: (err: AWSError, data: SageMaker.Types.ListDomainsResponse) => void): Request<SageMaker.Types.ListDomainsResponse, AWSError>;
  /**
   * Lists all edge deployment plans.
   */
  listEdgeDeploymentPlans(params: SageMaker.Types.ListEdgeDeploymentPlansRequest, callback?: (err: AWSError, data: SageMaker.Types.ListEdgeDeploymentPlansResponse) => void): Request<SageMaker.Types.ListEdgeDeploymentPlansResponse, AWSError>;
  /**
   * Lists all edge deployment plans.
   */
  listEdgeDeploymentPlans(callback?: (err: AWSError, data: SageMaker.Types.ListEdgeDeploymentPlansResponse) => void): Request<SageMaker.Types.ListEdgeDeploymentPlansResponse, AWSError>;
  /**
   * Returns a list of edge packaging jobs.
   */
  listEdgePackagingJobs(params: SageMaker.Types.ListEdgePackagingJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListEdgePackagingJobsResponse) => void): Request<SageMaker.Types.ListEdgePackagingJobsResponse, AWSError>;
  /**
   * Returns a list of edge packaging jobs.
   */
  listEdgePackagingJobs(callback?: (err: AWSError, data: SageMaker.Types.ListEdgePackagingJobsResponse) => void): Request<SageMaker.Types.ListEdgePackagingJobsResponse, AWSError>;
  /**
   * Lists endpoint configurations.
   */
  listEndpointConfigs(params: SageMaker.Types.ListEndpointConfigsInput, callback?: (err: AWSError, data: SageMaker.Types.ListEndpointConfigsOutput) => void): Request<SageMaker.Types.ListEndpointConfigsOutput, AWSError>;
  /**
   * Lists endpoint configurations.
   */
  listEndpointConfigs(callback?: (err: AWSError, data: SageMaker.Types.ListEndpointConfigsOutput) => void): Request<SageMaker.Types.ListEndpointConfigsOutput, AWSError>;
  /**
   * Lists endpoints.
   */
  listEndpoints(params: SageMaker.Types.ListEndpointsInput, callback?: (err: AWSError, data: SageMaker.Types.ListEndpointsOutput) => void): Request<SageMaker.Types.ListEndpointsOutput, AWSError>;
  /**
   * Lists endpoints.
   */
  listEndpoints(callback?: (err: AWSError, data: SageMaker.Types.ListEndpointsOutput) => void): Request<SageMaker.Types.ListEndpointsOutput, AWSError>;
  /**
   * Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
   */
  listExperiments(params: SageMaker.Types.ListExperimentsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListExperimentsResponse) => void): Request<SageMaker.Types.ListExperimentsResponse, AWSError>;
  /**
   * Lists all the experiments in your account. The list can be filtered to show only experiments that were created in a specific time range. The list can be sorted by experiment name or creation time.
   */
  listExperiments(callback?: (err: AWSError, data: SageMaker.Types.ListExperimentsResponse) => void): Request<SageMaker.Types.ListExperimentsResponse, AWSError>;
  /**
   * List FeatureGroups based on given filter and order.
   */
  listFeatureGroups(params: SageMaker.Types.ListFeatureGroupsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListFeatureGroupsResponse) => void): Request<SageMaker.Types.ListFeatureGroupsResponse, AWSError>;
  /**
   * List FeatureGroups based on given filter and order.
   */
  listFeatureGroups(callback?: (err: AWSError, data: SageMaker.Types.ListFeatureGroupsResponse) => void): Request<SageMaker.Types.ListFeatureGroupsResponse, AWSError>;
  /**
   * Returns information about the flow definitions in your account.
   */
  listFlowDefinitions(params: SageMaker.Types.ListFlowDefinitionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListFlowDefinitionsResponse) => void): Request<SageMaker.Types.ListFlowDefinitionsResponse, AWSError>;
  /**
   * Returns information about the flow definitions in your account.
   */
  listFlowDefinitions(callback?: (err: AWSError, data: SageMaker.Types.ListFlowDefinitionsResponse) => void): Request<SageMaker.Types.ListFlowDefinitionsResponse, AWSError>;
  /**
   * List hub content versions.
   */
  listHubContentVersions(params: SageMaker.Types.ListHubContentVersionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListHubContentVersionsResponse) => void): Request<SageMaker.Types.ListHubContentVersionsResponse, AWSError>;
  /**
   * List hub content versions.
   */
  listHubContentVersions(callback?: (err: AWSError, data: SageMaker.Types.ListHubContentVersionsResponse) => void): Request<SageMaker.Types.ListHubContentVersionsResponse, AWSError>;
  /**
   * List the contents of a hub.
   */
  listHubContents(params: SageMaker.Types.ListHubContentsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListHubContentsResponse) => void): Request<SageMaker.Types.ListHubContentsResponse, AWSError>;
  /**
   * List the contents of a hub.
   */
  listHubContents(callback?: (err: AWSError, data: SageMaker.Types.ListHubContentsResponse) => void): Request<SageMaker.Types.ListHubContentsResponse, AWSError>;
  /**
   * List all existing hubs.
   */
  listHubs(params: SageMaker.Types.ListHubsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListHubsResponse) => void): Request<SageMaker.Types.ListHubsResponse, AWSError>;
  /**
   * List all existing hubs.
   */
  listHubs(callback?: (err: AWSError, data: SageMaker.Types.ListHubsResponse) => void): Request<SageMaker.Types.ListHubsResponse, AWSError>;
  /**
   * Returns information about the human task user interfaces in your account.
   */
  listHumanTaskUis(params: SageMaker.Types.ListHumanTaskUisRequest, callback?: (err: AWSError, data: SageMaker.Types.ListHumanTaskUisResponse) => void): Request<SageMaker.Types.ListHumanTaskUisResponse, AWSError>;
  /**
   * Returns information about the human task user interfaces in your account.
   */
  listHumanTaskUis(callback?: (err: AWSError, data: SageMaker.Types.ListHumanTaskUisResponse) => void): Request<SageMaker.Types.ListHumanTaskUisResponse, AWSError>;
  /**
   * Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
   */
  listHyperParameterTuningJobs(params: SageMaker.Types.ListHyperParameterTuningJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListHyperParameterTuningJobsResponse) => void): Request<SageMaker.Types.ListHyperParameterTuningJobsResponse, AWSError>;
  /**
   * Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
   */
  listHyperParameterTuningJobs(callback?: (err: AWSError, data: SageMaker.Types.ListHyperParameterTuningJobsResponse) => void): Request<SageMaker.Types.ListHyperParameterTuningJobsResponse, AWSError>;
  /**
   * Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
   */
  listImageVersions(params: SageMaker.Types.ListImageVersionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListImageVersionsResponse) => void): Request<SageMaker.Types.ListImageVersionsResponse, AWSError>;
  /**
   * Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
   */
  listImageVersions(callback?: (err: AWSError, data: SageMaker.Types.ListImageVersionsResponse) => void): Request<SageMaker.Types.ListImageVersionsResponse, AWSError>;
  /**
   * Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
   */
  listImages(params: SageMaker.Types.ListImagesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListImagesResponse) => void): Request<SageMaker.Types.ListImagesResponse, AWSError>;
  /**
   * Lists the images in your account and their properties. The list can be filtered by creation time or modified time, and whether the image name contains a specified string.
   */
  listImages(callback?: (err: AWSError, data: SageMaker.Types.ListImagesResponse) => void): Request<SageMaker.Types.ListImagesResponse, AWSError>;
  /**
   * Lists the inference components in your account and their properties.
   */
  listInferenceComponents(params: SageMaker.Types.ListInferenceComponentsInput, callback?: (err: AWSError, data: SageMaker.Types.ListInferenceComponentsOutput) => void): Request<SageMaker.Types.ListInferenceComponentsOutput, AWSError>;
  /**
   * Lists the inference components in your account and their properties.
   */
  listInferenceComponents(callback?: (err: AWSError, data: SageMaker.Types.ListInferenceComponentsOutput) => void): Request<SageMaker.Types.ListInferenceComponentsOutput, AWSError>;
  /**
   * Returns the list of all inference experiments.
   */
  listInferenceExperiments(params: SageMaker.Types.ListInferenceExperimentsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListInferenceExperimentsResponse) => void): Request<SageMaker.Types.ListInferenceExperimentsResponse, AWSError>;
  /**
   * Returns the list of all inference experiments.
   */
  listInferenceExperiments(callback?: (err: AWSError, data: SageMaker.Types.ListInferenceExperimentsResponse) => void): Request<SageMaker.Types.ListInferenceExperimentsResponse, AWSError>;
  /**
   * Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
   */
  listInferenceRecommendationsJobSteps(params: SageMaker.Types.ListInferenceRecommendationsJobStepsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListInferenceRecommendationsJobStepsResponse) => void): Request<SageMaker.Types.ListInferenceRecommendationsJobStepsResponse, AWSError>;
  /**
   * Returns a list of the subtasks for an Inference Recommender job. The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.
   */
  listInferenceRecommendationsJobSteps(callback?: (err: AWSError, data: SageMaker.Types.ListInferenceRecommendationsJobStepsResponse) => void): Request<SageMaker.Types.ListInferenceRecommendationsJobStepsResponse, AWSError>;
  /**
   * Lists recommendation jobs that satisfy various filters.
   */
  listInferenceRecommendationsJobs(params: SageMaker.Types.ListInferenceRecommendationsJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListInferenceRecommendationsJobsResponse) => void): Request<SageMaker.Types.ListInferenceRecommendationsJobsResponse, AWSError>;
  /**
   * Lists recommendation jobs that satisfy various filters.
   */
  listInferenceRecommendationsJobs(callback?: (err: AWSError, data: SageMaker.Types.ListInferenceRecommendationsJobsResponse) => void): Request<SageMaker.Types.ListInferenceRecommendationsJobsResponse, AWSError>;
  /**
   * Gets a list of labeling jobs.
   */
  listLabelingJobs(params: SageMaker.Types.ListLabelingJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListLabelingJobsResponse) => void): Request<SageMaker.Types.ListLabelingJobsResponse, AWSError>;
  /**
   * Gets a list of labeling jobs.
   */
  listLabelingJobs(callback?: (err: AWSError, data: SageMaker.Types.ListLabelingJobsResponse) => void): Request<SageMaker.Types.ListLabelingJobsResponse, AWSError>;
  /**
   * Gets a list of labeling jobs assigned to a specified work team.
   */
  listLabelingJobsForWorkteam(params: SageMaker.Types.ListLabelingJobsForWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.ListLabelingJobsForWorkteamResponse) => void): Request<SageMaker.Types.ListLabelingJobsForWorkteamResponse, AWSError>;
  /**
   * Gets a list of labeling jobs assigned to a specified work team.
   */
  listLabelingJobsForWorkteam(callback?: (err: AWSError, data: SageMaker.Types.ListLabelingJobsForWorkteamResponse) => void): Request<SageMaker.Types.ListLabelingJobsForWorkteamResponse, AWSError>;
  /**
   * A list of lineage groups shared with your Amazon Web Services account. For more information, see  Cross-Account Lineage Tracking  in the Amazon SageMaker Developer Guide.
   */
  listLineageGroups(params: SageMaker.Types.ListLineageGroupsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListLineageGroupsResponse) => void): Request<SageMaker.Types.ListLineageGroupsResponse, AWSError>;
  /**
   * A list of lineage groups shared with your Amazon Web Services account. For more information, see  Cross-Account Lineage Tracking  in the Amazon SageMaker Developer Guide.
   */
  listLineageGroups(callback?: (err: AWSError, data: SageMaker.Types.ListLineageGroupsResponse) => void): Request<SageMaker.Types.ListLineageGroupsResponse, AWSError>;
  /**
   * Lists all MLflow Tracking Servers.
   */
  listMlflowTrackingServers(params: SageMaker.Types.ListMlflowTrackingServersRequest, callback?: (err: AWSError, data: SageMaker.Types.ListMlflowTrackingServersResponse) => void): Request<SageMaker.Types.ListMlflowTrackingServersResponse, AWSError>;
  /**
   * Lists all MLflow Tracking Servers.
   */
  listMlflowTrackingServers(callback?: (err: AWSError, data: SageMaker.Types.ListMlflowTrackingServersResponse) => void): Request<SageMaker.Types.ListMlflowTrackingServersResponse, AWSError>;
  /**
   * Lists model bias jobs definitions that satisfy various filters.
   */
  listModelBiasJobDefinitions(params: SageMaker.Types.ListModelBiasJobDefinitionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelBiasJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelBiasJobDefinitionsResponse, AWSError>;
  /**
   * Lists model bias jobs definitions that satisfy various filters.
   */
  listModelBiasJobDefinitions(callback?: (err: AWSError, data: SageMaker.Types.ListModelBiasJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelBiasJobDefinitionsResponse, AWSError>;
  /**
   * List the export jobs for the Amazon SageMaker Model Card.
   */
  listModelCardExportJobs(params: SageMaker.Types.ListModelCardExportJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelCardExportJobsResponse) => void): Request<SageMaker.Types.ListModelCardExportJobsResponse, AWSError>;
  /**
   * List the export jobs for the Amazon SageMaker Model Card.
   */
  listModelCardExportJobs(callback?: (err: AWSError, data: SageMaker.Types.ListModelCardExportJobsResponse) => void): Request<SageMaker.Types.ListModelCardExportJobsResponse, AWSError>;
  /**
   * List existing versions of an Amazon SageMaker Model Card.
   */
  listModelCardVersions(params: SageMaker.Types.ListModelCardVersionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelCardVersionsResponse) => void): Request<SageMaker.Types.ListModelCardVersionsResponse, AWSError>;
  /**
   * List existing versions of an Amazon SageMaker Model Card.
   */
  listModelCardVersions(callback?: (err: AWSError, data: SageMaker.Types.ListModelCardVersionsResponse) => void): Request<SageMaker.Types.ListModelCardVersionsResponse, AWSError>;
  /**
   * List existing model cards.
   */
  listModelCards(params: SageMaker.Types.ListModelCardsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelCardsResponse) => void): Request<SageMaker.Types.ListModelCardsResponse, AWSError>;
  /**
   * List existing model cards.
   */
  listModelCards(callback?: (err: AWSError, data: SageMaker.Types.ListModelCardsResponse) => void): Request<SageMaker.Types.ListModelCardsResponse, AWSError>;
  /**
   * Lists model explainability job definitions that satisfy various filters.
   */
  listModelExplainabilityJobDefinitions(params: SageMaker.Types.ListModelExplainabilityJobDefinitionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelExplainabilityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelExplainabilityJobDefinitionsResponse, AWSError>;
  /**
   * Lists model explainability job definitions that satisfy various filters.
   */
  listModelExplainabilityJobDefinitions(callback?: (err: AWSError, data: SageMaker.Types.ListModelExplainabilityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelExplainabilityJobDefinitionsResponse, AWSError>;
  /**
   * Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
   */
  listModelMetadata(params: SageMaker.Types.ListModelMetadataRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelMetadataResponse) => void): Request<SageMaker.Types.ListModelMetadataResponse, AWSError>;
  /**
   * Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.
   */
  listModelMetadata(callback?: (err: AWSError, data: SageMaker.Types.ListModelMetadataResponse) => void): Request<SageMaker.Types.ListModelMetadataResponse, AWSError>;
  /**
   * Gets a list of the model groups in your Amazon Web Services account.
   */
  listModelPackageGroups(params: SageMaker.Types.ListModelPackageGroupsInput, callback?: (err: AWSError, data: SageMaker.Types.ListModelPackageGroupsOutput) => void): Request<SageMaker.Types.ListModelPackageGroupsOutput, AWSError>;
  /**
   * Gets a list of the model groups in your Amazon Web Services account.
   */
  listModelPackageGroups(callback?: (err: AWSError, data: SageMaker.Types.ListModelPackageGroupsOutput) => void): Request<SageMaker.Types.ListModelPackageGroupsOutput, AWSError>;
  /**
   * Lists the model packages that have been created.
   */
  listModelPackages(params: SageMaker.Types.ListModelPackagesInput, callback?: (err: AWSError, data: SageMaker.Types.ListModelPackagesOutput) => void): Request<SageMaker.Types.ListModelPackagesOutput, AWSError>;
  /**
   * Lists the model packages that have been created.
   */
  listModelPackages(callback?: (err: AWSError, data: SageMaker.Types.ListModelPackagesOutput) => void): Request<SageMaker.Types.ListModelPackagesOutput, AWSError>;
  /**
   * Gets a list of model quality monitoring job definitions in your account.
   */
  listModelQualityJobDefinitions(params: SageMaker.Types.ListModelQualityJobDefinitionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListModelQualityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelQualityJobDefinitionsResponse, AWSError>;
  /**
   * Gets a list of model quality monitoring job definitions in your account.
   */
  listModelQualityJobDefinitions(callback?: (err: AWSError, data: SageMaker.Types.ListModelQualityJobDefinitionsResponse) => void): Request<SageMaker.Types.ListModelQualityJobDefinitionsResponse, AWSError>;
  /**
   * Lists models created with the CreateModel API.
   */
  listModels(params: SageMaker.Types.ListModelsInput, callback?: (err: AWSError, data: SageMaker.Types.ListModelsOutput) => void): Request<SageMaker.Types.ListModelsOutput, AWSError>;
  /**
   * Lists models created with the CreateModel API.
   */
  listModels(callback?: (err: AWSError, data: SageMaker.Types.ListModelsOutput) => void): Request<SageMaker.Types.ListModelsOutput, AWSError>;
  /**
   * Gets a list of past alerts in a model monitoring schedule.
   */
  listMonitoringAlertHistory(params: SageMaker.Types.ListMonitoringAlertHistoryRequest, callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringAlertHistoryResponse) => void): Request<SageMaker.Types.ListMonitoringAlertHistoryResponse, AWSError>;
  /**
   * Gets a list of past alerts in a model monitoring schedule.
   */
  listMonitoringAlertHistory(callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringAlertHistoryResponse) => void): Request<SageMaker.Types.ListMonitoringAlertHistoryResponse, AWSError>;
  /**
   * Gets the alerts for a single monitoring schedule.
   */
  listMonitoringAlerts(params: SageMaker.Types.ListMonitoringAlertsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringAlertsResponse) => void): Request<SageMaker.Types.ListMonitoringAlertsResponse, AWSError>;
  /**
   * Gets the alerts for a single monitoring schedule.
   */
  listMonitoringAlerts(callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringAlertsResponse) => void): Request<SageMaker.Types.ListMonitoringAlertsResponse, AWSError>;
  /**
   * Returns list of all monitoring job executions.
   */
  listMonitoringExecutions(params: SageMaker.Types.ListMonitoringExecutionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringExecutionsResponse) => void): Request<SageMaker.Types.ListMonitoringExecutionsResponse, AWSError>;
  /**
   * Returns list of all monitoring job executions.
   */
  listMonitoringExecutions(callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringExecutionsResponse) => void): Request<SageMaker.Types.ListMonitoringExecutionsResponse, AWSError>;
  /**
   * Returns list of all monitoring schedules.
   */
  listMonitoringSchedules(params: SageMaker.Types.ListMonitoringSchedulesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringSchedulesResponse) => void): Request<SageMaker.Types.ListMonitoringSchedulesResponse, AWSError>;
  /**
   * Returns list of all monitoring schedules.
   */
  listMonitoringSchedules(callback?: (err: AWSError, data: SageMaker.Types.ListMonitoringSchedulesResponse) => void): Request<SageMaker.Types.ListMonitoringSchedulesResponse, AWSError>;
  /**
   * Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
   */
  listNotebookInstanceLifecycleConfigs(params: SageMaker.Types.ListNotebookInstanceLifecycleConfigsInput, callback?: (err: AWSError, data: SageMaker.Types.ListNotebookInstanceLifecycleConfigsOutput) => void): Request<SageMaker.Types.ListNotebookInstanceLifecycleConfigsOutput, AWSError>;
  /**
   * Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
   */
  listNotebookInstanceLifecycleConfigs(callback?: (err: AWSError, data: SageMaker.Types.ListNotebookInstanceLifecycleConfigsOutput) => void): Request<SageMaker.Types.ListNotebookInstanceLifecycleConfigsOutput, AWSError>;
  /**
   * Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region. 
   */
  listNotebookInstances(params: SageMaker.Types.ListNotebookInstancesInput, callback?: (err: AWSError, data: SageMaker.Types.ListNotebookInstancesOutput) => void): Request<SageMaker.Types.ListNotebookInstancesOutput, AWSError>;
  /**
   * Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region. 
   */
  listNotebookInstances(callback?: (err: AWSError, data: SageMaker.Types.ListNotebookInstancesOutput) => void): Request<SageMaker.Types.ListNotebookInstancesOutput, AWSError>;
  /**
   * Lists the optimization jobs in your account and their properties.
   */
  listOptimizationJobs(params: SageMaker.Types.ListOptimizationJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListOptimizationJobsResponse) => void): Request<SageMaker.Types.ListOptimizationJobsResponse, AWSError>;
  /**
   * Lists the optimization jobs in your account and their properties.
   */
  listOptimizationJobs(callback?: (err: AWSError, data: SageMaker.Types.ListOptimizationJobsResponse) => void): Request<SageMaker.Types.ListOptimizationJobsResponse, AWSError>;
  /**
   * Gets a list of PipeLineExecutionStep objects.
   */
  listPipelineExecutionSteps(params: SageMaker.Types.ListPipelineExecutionStepsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListPipelineExecutionStepsResponse) => void): Request<SageMaker.Types.ListPipelineExecutionStepsResponse, AWSError>;
  /**
   * Gets a list of PipeLineExecutionStep objects.
   */
  listPipelineExecutionSteps(callback?: (err: AWSError, data: SageMaker.Types.ListPipelineExecutionStepsResponse) => void): Request<SageMaker.Types.ListPipelineExecutionStepsResponse, AWSError>;
  /**
   * Gets a list of the pipeline executions.
   */
  listPipelineExecutions(params: SageMaker.Types.ListPipelineExecutionsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListPipelineExecutionsResponse) => void): Request<SageMaker.Types.ListPipelineExecutionsResponse, AWSError>;
  /**
   * Gets a list of the pipeline executions.
   */
  listPipelineExecutions(callback?: (err: AWSError, data: SageMaker.Types.ListPipelineExecutionsResponse) => void): Request<SageMaker.Types.ListPipelineExecutionsResponse, AWSError>;
  /**
   * Gets a list of parameters for a pipeline execution.
   */
  listPipelineParametersForExecution(params: SageMaker.Types.ListPipelineParametersForExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.ListPipelineParametersForExecutionResponse) => void): Request<SageMaker.Types.ListPipelineParametersForExecutionResponse, AWSError>;
  /**
   * Gets a list of parameters for a pipeline execution.
   */
  listPipelineParametersForExecution(callback?: (err: AWSError, data: SageMaker.Types.ListPipelineParametersForExecutionResponse) => void): Request<SageMaker.Types.ListPipelineParametersForExecutionResponse, AWSError>;
  /**
   * Gets a list of pipelines.
   */
  listPipelines(params: SageMaker.Types.ListPipelinesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListPipelinesResponse) => void): Request<SageMaker.Types.ListPipelinesResponse, AWSError>;
  /**
   * Gets a list of pipelines.
   */
  listPipelines(callback?: (err: AWSError, data: SageMaker.Types.ListPipelinesResponse) => void): Request<SageMaker.Types.ListPipelinesResponse, AWSError>;
  /**
   * Lists processing jobs that satisfy various filters.
   */
  listProcessingJobs(params: SageMaker.Types.ListProcessingJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListProcessingJobsResponse) => void): Request<SageMaker.Types.ListProcessingJobsResponse, AWSError>;
  /**
   * Lists processing jobs that satisfy various filters.
   */
  listProcessingJobs(callback?: (err: AWSError, data: SageMaker.Types.ListProcessingJobsResponse) => void): Request<SageMaker.Types.ListProcessingJobsResponse, AWSError>;
  /**
   * Gets a list of the projects in an Amazon Web Services account.
   */
  listProjects(params: SageMaker.Types.ListProjectsInput, callback?: (err: AWSError, data: SageMaker.Types.ListProjectsOutput) => void): Request<SageMaker.Types.ListProjectsOutput, AWSError>;
  /**
   * Gets a list of the projects in an Amazon Web Services account.
   */
  listProjects(callback?: (err: AWSError, data: SageMaker.Types.ListProjectsOutput) => void): Request<SageMaker.Types.ListProjectsOutput, AWSError>;
  /**
   *  Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000. 
   */
  listResourceCatalogs(params: SageMaker.Types.ListResourceCatalogsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListResourceCatalogsResponse) => void): Request<SageMaker.Types.ListResourceCatalogsResponse, AWSError>;
  /**
   *  Lists Amazon SageMaker Catalogs based on given filters and orders. The maximum number of ResourceCatalogs viewable is 1000. 
   */
  listResourceCatalogs(callback?: (err: AWSError, data: SageMaker.Types.ListResourceCatalogsResponse) => void): Request<SageMaker.Types.ListResourceCatalogsResponse, AWSError>;
  /**
   * Lists spaces.
   */
  listSpaces(params: SageMaker.Types.ListSpacesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListSpacesResponse) => void): Request<SageMaker.Types.ListSpacesResponse, AWSError>;
  /**
   * Lists spaces.
   */
  listSpaces(callback?: (err: AWSError, data: SageMaker.Types.ListSpacesResponse) => void): Request<SageMaker.Types.ListSpacesResponse, AWSError>;
  /**
   * Lists devices allocated to the stage, containing detailed device information and deployment status.
   */
  listStageDevices(params: SageMaker.Types.ListStageDevicesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListStageDevicesResponse) => void): Request<SageMaker.Types.ListStageDevicesResponse, AWSError>;
  /**
   * Lists devices allocated to the stage, containing detailed device information and deployment status.
   */
  listStageDevices(callback?: (err: AWSError, data: SageMaker.Types.ListStageDevicesResponse) => void): Request<SageMaker.Types.ListStageDevicesResponse, AWSError>;
  /**
   * Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
   */
  listStudioLifecycleConfigs(params: SageMaker.Types.ListStudioLifecycleConfigsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListStudioLifecycleConfigsResponse) => void): Request<SageMaker.Types.ListStudioLifecycleConfigsResponse, AWSError>;
  /**
   * Lists the Amazon SageMaker Studio Lifecycle Configurations in your Amazon Web Services Account.
   */
  listStudioLifecycleConfigs(callback?: (err: AWSError, data: SageMaker.Types.ListStudioLifecycleConfigsResponse) => void): Request<SageMaker.Types.ListStudioLifecycleConfigsResponse, AWSError>;
  /**
   * Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
   */
  listSubscribedWorkteams(params: SageMaker.Types.ListSubscribedWorkteamsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListSubscribedWorkteamsResponse) => void): Request<SageMaker.Types.ListSubscribedWorkteamsResponse, AWSError>;
  /**
   * Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
   */
  listSubscribedWorkteams(callback?: (err: AWSError, data: SageMaker.Types.ListSubscribedWorkteamsResponse) => void): Request<SageMaker.Types.ListSubscribedWorkteamsResponse, AWSError>;
  /**
   * Returns the tags for the specified SageMaker resource.
   */
  listTags(params: SageMaker.Types.ListTagsInput, callback?: (err: AWSError, data: SageMaker.Types.ListTagsOutput) => void): Request<SageMaker.Types.ListTagsOutput, AWSError>;
  /**
   * Returns the tags for the specified SageMaker resource.
   */
  listTags(callback?: (err: AWSError, data: SageMaker.Types.ListTagsOutput) => void): Request<SageMaker.Types.ListTagsOutput, AWSError>;
  /**
   * Lists training jobs.  When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters:  { ... MaxResults: 100, StatusEquals: InProgress ... }  First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code.  aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress  
   */
  listTrainingJobs(params: SageMaker.Types.ListTrainingJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListTrainingJobsResponse) => void): Request<SageMaker.Types.ListTrainingJobsResponse, AWSError>;
  /**
   * Lists training jobs.  When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response. For example, if ListTrainingJobs is invoked with the following parameters:  { ... MaxResults: 100, StatusEquals: InProgress ... }  First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned. You can quickly test the API using the following Amazon Web Services CLI code.  aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress  
   */
  listTrainingJobs(callback?: (err: AWSError, data: SageMaker.Types.ListTrainingJobsResponse) => void): Request<SageMaker.Types.ListTrainingJobsResponse, AWSError>;
  /**
   * Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
   */
  listTrainingJobsForHyperParameterTuningJob(params: SageMaker.Types.ListTrainingJobsForHyperParameterTuningJobRequest, callback?: (err: AWSError, data: SageMaker.Types.ListTrainingJobsForHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.ListTrainingJobsForHyperParameterTuningJobResponse, AWSError>;
  /**
   * Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
   */
  listTrainingJobsForHyperParameterTuningJob(callback?: (err: AWSError, data: SageMaker.Types.ListTrainingJobsForHyperParameterTuningJobResponse) => void): Request<SageMaker.Types.ListTrainingJobsForHyperParameterTuningJobResponse, AWSError>;
  /**
   * Lists transform jobs.
   */
  listTransformJobs(params: SageMaker.Types.ListTransformJobsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListTransformJobsResponse) => void): Request<SageMaker.Types.ListTransformJobsResponse, AWSError>;
  /**
   * Lists transform jobs.
   */
  listTransformJobs(callback?: (err: AWSError, data: SageMaker.Types.ListTransformJobsResponse) => void): Request<SageMaker.Types.ListTransformJobsResponse, AWSError>;
  /**
   * Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:    ExperimentName     SourceArn     TrialName   
   */
  listTrialComponents(params: SageMaker.Types.ListTrialComponentsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListTrialComponentsResponse) => void): Request<SageMaker.Types.ListTrialComponentsResponse, AWSError>;
  /**
   * Lists the trial components in your account. You can sort the list by trial component name or creation time. You can filter the list to show only components that were created in a specific time range. You can also filter on one of the following:    ExperimentName     SourceArn     TrialName   
   */
  listTrialComponents(callback?: (err: AWSError, data: SageMaker.Types.ListTrialComponentsResponse) => void): Request<SageMaker.Types.ListTrialComponentsResponse, AWSError>;
  /**
   * Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
   */
  listTrials(params: SageMaker.Types.ListTrialsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListTrialsResponse) => void): Request<SageMaker.Types.ListTrialsResponse, AWSError>;
  /**
   * Lists the trials in your account. Specify an experiment name to limit the list to the trials that are part of that experiment. Specify a trial component name to limit the list to the trials that associated with that trial component. The list can be filtered to show only trials that were created in a specific time range. The list can be sorted by trial name or creation time.
   */
  listTrials(callback?: (err: AWSError, data: SageMaker.Types.ListTrialsResponse) => void): Request<SageMaker.Types.ListTrialsResponse, AWSError>;
  /**
   * Lists user profiles.
   */
  listUserProfiles(params: SageMaker.Types.ListUserProfilesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListUserProfilesResponse) => void): Request<SageMaker.Types.ListUserProfilesResponse, AWSError>;
  /**
   * Lists user profiles.
   */
  listUserProfiles(callback?: (err: AWSError, data: SageMaker.Types.ListUserProfilesResponse) => void): Request<SageMaker.Types.ListUserProfilesResponse, AWSError>;
  /**
   * Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
   */
  listWorkforces(params: SageMaker.Types.ListWorkforcesRequest, callback?: (err: AWSError, data: SageMaker.Types.ListWorkforcesResponse) => void): Request<SageMaker.Types.ListWorkforcesResponse, AWSError>;
  /**
   * Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.
   */
  listWorkforces(callback?: (err: AWSError, data: SageMaker.Types.ListWorkforcesResponse) => void): Request<SageMaker.Types.ListWorkforcesResponse, AWSError>;
  /**
   * Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
   */
  listWorkteams(params: SageMaker.Types.ListWorkteamsRequest, callback?: (err: AWSError, data: SageMaker.Types.ListWorkteamsResponse) => void): Request<SageMaker.Types.ListWorkteamsResponse, AWSError>;
  /**
   * Gets a list of private work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
   */
  listWorkteams(callback?: (err: AWSError, data: SageMaker.Types.ListWorkteamsResponse) => void): Request<SageMaker.Types.ListWorkteamsResponse, AWSError>;
  /**
   * Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
   */
  putModelPackageGroupPolicy(params: SageMaker.Types.PutModelPackageGroupPolicyInput, callback?: (err: AWSError, data: SageMaker.Types.PutModelPackageGroupPolicyOutput) => void): Request<SageMaker.Types.PutModelPackageGroupPolicyOutput, AWSError>;
  /**
   * Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..
   */
  putModelPackageGroupPolicy(callback?: (err: AWSError, data: SageMaker.Types.PutModelPackageGroupPolicyOutput) => void): Request<SageMaker.Types.PutModelPackageGroupPolicyOutput, AWSError>;
  /**
   * Use this action to inspect your lineage and discover relationships between entities. For more information, see  Querying Lineage Entities in the Amazon SageMaker Developer Guide.
   */
  queryLineage(params: SageMaker.Types.QueryLineageRequest, callback?: (err: AWSError, data: SageMaker.Types.QueryLineageResponse) => void): Request<SageMaker.Types.QueryLineageResponse, AWSError>;
  /**
   * Use this action to inspect your lineage and discover relationships between entities. For more information, see  Querying Lineage Entities in the Amazon SageMaker Developer Guide.
   */
  queryLineage(callback?: (err: AWSError, data: SageMaker.Types.QueryLineageResponse) => void): Request<SageMaker.Types.QueryLineageResponse, AWSError>;
  /**
   * Register devices.
   */
  registerDevices(params: SageMaker.Types.RegisterDevicesRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Register devices.
   */
  registerDevices(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Renders the UI template so that you can preview the worker's experience. 
   */
  renderUiTemplate(params: SageMaker.Types.RenderUiTemplateRequest, callback?: (err: AWSError, data: SageMaker.Types.RenderUiTemplateResponse) => void): Request<SageMaker.Types.RenderUiTemplateResponse, AWSError>;
  /**
   * Renders the UI template so that you can preview the worker's experience. 
   */
  renderUiTemplate(callback?: (err: AWSError, data: SageMaker.Types.RenderUiTemplateResponse) => void): Request<SageMaker.Types.RenderUiTemplateResponse, AWSError>;
  /**
   * Retry the execution of the pipeline.
   */
  retryPipelineExecution(params: SageMaker.Types.RetryPipelineExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.RetryPipelineExecutionResponse) => void): Request<SageMaker.Types.RetryPipelineExecutionResponse, AWSError>;
  /**
   * Retry the execution of the pipeline.
   */
  retryPipelineExecution(callback?: (err: AWSError, data: SageMaker.Types.RetryPipelineExecutionResponse) => void): Request<SageMaker.Types.RetryPipelineExecutionResponse, AWSError>;
  /**
   * Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp.  The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information. 
   */
  search(params: SageMaker.Types.SearchRequest, callback?: (err: AWSError, data: SageMaker.Types.SearchResponse) => void): Request<SageMaker.Types.SearchResponse, AWSError>;
  /**
   * Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order. You can query against the following value types: numeric, text, Boolean, and timestamp.  The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information. 
   */
  search(callback?: (err: AWSError, data: SageMaker.Types.SearchResponse) => void): Request<SageMaker.Types.SearchResponse, AWSError>;
  /**
   * Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
   */
  sendPipelineExecutionStepFailure(params: SageMaker.Types.SendPipelineExecutionStepFailureRequest, callback?: (err: AWSError, data: SageMaker.Types.SendPipelineExecutionStepFailureResponse) => void): Request<SageMaker.Types.SendPipelineExecutionStepFailureResponse, AWSError>;
  /**
   * Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
   */
  sendPipelineExecutionStepFailure(callback?: (err: AWSError, data: SageMaker.Types.SendPipelineExecutionStepFailureResponse) => void): Request<SageMaker.Types.SendPipelineExecutionStepFailureResponse, AWSError>;
  /**
   * Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
   */
  sendPipelineExecutionStepSuccess(params: SageMaker.Types.SendPipelineExecutionStepSuccessRequest, callback?: (err: AWSError, data: SageMaker.Types.SendPipelineExecutionStepSuccessResponse) => void): Request<SageMaker.Types.SendPipelineExecutionStepSuccessResponse, AWSError>;
  /**
   * Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).
   */
  sendPipelineExecutionStepSuccess(callback?: (err: AWSError, data: SageMaker.Types.SendPipelineExecutionStepSuccessResponse) => void): Request<SageMaker.Types.SendPipelineExecutionStepSuccessResponse, AWSError>;
  /**
   * Starts a stage in an edge deployment plan.
   */
  startEdgeDeploymentStage(params: SageMaker.Types.StartEdgeDeploymentStageRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a stage in an edge deployment plan.
   */
  startEdgeDeploymentStage(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts an inference experiment.
   */
  startInferenceExperiment(params: SageMaker.Types.StartInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.StartInferenceExperimentResponse) => void): Request<SageMaker.Types.StartInferenceExperimentResponse, AWSError>;
  /**
   * Starts an inference experiment.
   */
  startInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.StartInferenceExperimentResponse) => void): Request<SageMaker.Types.StartInferenceExperimentResponse, AWSError>;
  /**
   * Programmatically start an MLflow Tracking Server.
   */
  startMlflowTrackingServer(params: SageMaker.Types.StartMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.StartMlflowTrackingServerResponse) => void): Request<SageMaker.Types.StartMlflowTrackingServerResponse, AWSError>;
  /**
   * Programmatically start an MLflow Tracking Server.
   */
  startMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.StartMlflowTrackingServerResponse) => void): Request<SageMaker.Types.StartMlflowTrackingServerResponse, AWSError>;
  /**
   * Starts a previously stopped monitoring schedule.  By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled. 
   */
  startMonitoringSchedule(params: SageMaker.Types.StartMonitoringScheduleRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a previously stopped monitoring schedule.  By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled. 
   */
  startMonitoringSchedule(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook. 
   */
  startNotebookInstance(params: SageMaker.Types.StartNotebookInstanceInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook. 
   */
  startNotebookInstance(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Starts a pipeline execution.
   */
  startPipelineExecution(params: SageMaker.Types.StartPipelineExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.StartPipelineExecutionResponse) => void): Request<SageMaker.Types.StartPipelineExecutionResponse, AWSError>;
  /**
   * Starts a pipeline execution.
   */
  startPipelineExecution(callback?: (err: AWSError, data: SageMaker.Types.StartPipelineExecutionResponse) => void): Request<SageMaker.Types.StartPipelineExecutionResponse, AWSError>;
  /**
   * A method for forcing a running job to shut down.
   */
  stopAutoMLJob(params: SageMaker.Types.StopAutoMLJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * A method for forcing a running job to shut down.
   */
  stopAutoMLJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a model compilation job.  To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal. When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped. 
   */
  stopCompilationJob(params: SageMaker.Types.StopCompilationJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a model compilation job.  To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal. When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped. 
   */
  stopCompilationJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a stage in an edge deployment plan.
   */
  stopEdgeDeploymentStage(params: SageMaker.Types.StopEdgeDeploymentStageRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a stage in an edge deployment plan.
   */
  stopEdgeDeploymentStage(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Request to stop an edge packaging job.
   */
  stopEdgePackagingJob(params: SageMaker.Types.StopEdgePackagingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Request to stop an edge packaging job.
   */
  stopEdgePackagingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
   */
  stopHyperParameterTuningJob(params: SageMaker.Types.StopHyperParameterTuningJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched. All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.
   */
  stopHyperParameterTuningJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops an inference experiment.
   */
  stopInferenceExperiment(params: SageMaker.Types.StopInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.StopInferenceExperimentResponse) => void): Request<SageMaker.Types.StopInferenceExperimentResponse, AWSError>;
  /**
   * Stops an inference experiment.
   */
  stopInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.StopInferenceExperimentResponse) => void): Request<SageMaker.Types.StopInferenceExperimentResponse, AWSError>;
  /**
   * Stops an Inference Recommender job.
   */
  stopInferenceRecommendationsJob(params: SageMaker.Types.StopInferenceRecommendationsJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops an Inference Recommender job.
   */
  stopInferenceRecommendationsJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
   */
  stopLabelingJob(params: SageMaker.Types.StopLabelingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.
   */
  stopLabelingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Programmatically stop an MLflow Tracking Server.
   */
  stopMlflowTrackingServer(params: SageMaker.Types.StopMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.StopMlflowTrackingServerResponse) => void): Request<SageMaker.Types.StopMlflowTrackingServerResponse, AWSError>;
  /**
   * Programmatically stop an MLflow Tracking Server.
   */
  stopMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.StopMlflowTrackingServerResponse) => void): Request<SageMaker.Types.StopMlflowTrackingServerResponse, AWSError>;
  /**
   * Stops a previously started monitoring schedule.
   */
  stopMonitoringSchedule(params: SageMaker.Types.StopMonitoringScheduleRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a previously started monitoring schedule.
   */
  stopMonitoringSchedule(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance. To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work. 
   */
  stopNotebookInstance(params: SageMaker.Types.StopNotebookInstanceInput, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance. To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work. 
   */
  stopNotebookInstance(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Ends a running inference optimization job.
   */
  stopOptimizationJob(params: SageMaker.Types.StopOptimizationJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Ends a running inference optimization job.
   */
  stopOptimizationJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a pipeline execution.  Callback Step  A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.  Lambda Step  A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
   */
  stopPipelineExecution(params: SageMaker.Types.StopPipelineExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.StopPipelineExecutionResponse) => void): Request<SageMaker.Types.StopPipelineExecutionResponse, AWSError>;
  /**
   * Stops a pipeline execution.  Callback Step  A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping". You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure. Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.  Lambda Step  A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.
   */
  stopPipelineExecution(callback?: (err: AWSError, data: SageMaker.Types.StopPipelineExecutionResponse) => void): Request<SageMaker.Types.StopPipelineExecutionResponse, AWSError>;
  /**
   * Stops a processing job.
   */
  stopProcessingJob(params: SageMaker.Types.StopProcessingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a processing job.
   */
  stopProcessingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.  When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.
   */
  stopTrainingJob(params: SageMaker.Types.StopTrainingJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.  When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.
   */
  stopTrainingJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
   */
  stopTransformJob(params: SageMaker.Types.StopTransformJobRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Stops a batch transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
   */
  stopTransformJob(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates an action.
   */
  updateAction(params: SageMaker.Types.UpdateActionRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateActionResponse) => void): Request<SageMaker.Types.UpdateActionResponse, AWSError>;
  /**
   * Updates an action.
   */
  updateAction(callback?: (err: AWSError, data: SageMaker.Types.UpdateActionResponse) => void): Request<SageMaker.Types.UpdateActionResponse, AWSError>;
  /**
   * Updates the properties of an AppImageConfig.
   */
  updateAppImageConfig(params: SageMaker.Types.UpdateAppImageConfigRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateAppImageConfigResponse) => void): Request<SageMaker.Types.UpdateAppImageConfigResponse, AWSError>;
  /**
   * Updates the properties of an AppImageConfig.
   */
  updateAppImageConfig(callback?: (err: AWSError, data: SageMaker.Types.UpdateAppImageConfigResponse) => void): Request<SageMaker.Types.UpdateAppImageConfigResponse, AWSError>;
  /**
   * Updates an artifact.
   */
  updateArtifact(params: SageMaker.Types.UpdateArtifactRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateArtifactResponse) => void): Request<SageMaker.Types.UpdateArtifactResponse, AWSError>;
  /**
   * Updates an artifact.
   */
  updateArtifact(callback?: (err: AWSError, data: SageMaker.Types.UpdateArtifactResponse) => void): Request<SageMaker.Types.UpdateArtifactResponse, AWSError>;
  /**
   * Updates a SageMaker HyperPod cluster.
   */
  updateCluster(params: SageMaker.Types.UpdateClusterRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateClusterResponse) => void): Request<SageMaker.Types.UpdateClusterResponse, AWSError>;
  /**
   * Updates a SageMaker HyperPod cluster.
   */
  updateCluster(callback?: (err: AWSError, data: SageMaker.Types.UpdateClusterResponse) => void): Request<SageMaker.Types.UpdateClusterResponse, AWSError>;
  /**
   * Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
   */
  updateClusterSoftware(params: SageMaker.Types.UpdateClusterSoftwareRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateClusterSoftwareResponse) => void): Request<SageMaker.Types.UpdateClusterSoftwareResponse, AWSError>;
  /**
   * Updates the platform software of a SageMaker HyperPod cluster for security patching. To learn how to use this API, see Update the SageMaker HyperPod platform software of a cluster.
   */
  updateClusterSoftware(callback?: (err: AWSError, data: SageMaker.Types.UpdateClusterSoftwareResponse) => void): Request<SageMaker.Types.UpdateClusterSoftwareResponse, AWSError>;
  /**
   * Updates the specified Git repository with the specified values.
   */
  updateCodeRepository(params: SageMaker.Types.UpdateCodeRepositoryInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateCodeRepositoryOutput) => void): Request<SageMaker.Types.UpdateCodeRepositoryOutput, AWSError>;
  /**
   * Updates the specified Git repository with the specified values.
   */
  updateCodeRepository(callback?: (err: AWSError, data: SageMaker.Types.UpdateCodeRepositoryOutput) => void): Request<SageMaker.Types.UpdateCodeRepositoryOutput, AWSError>;
  /**
   * Updates a context.
   */
  updateContext(params: SageMaker.Types.UpdateContextRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateContextResponse) => void): Request<SageMaker.Types.UpdateContextResponse, AWSError>;
  /**
   * Updates a context.
   */
  updateContext(callback?: (err: AWSError, data: SageMaker.Types.UpdateContextResponse) => void): Request<SageMaker.Types.UpdateContextResponse, AWSError>;
  /**
   * Updates a fleet of devices.
   */
  updateDeviceFleet(params: SageMaker.Types.UpdateDeviceFleetRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates a fleet of devices.
   */
  updateDeviceFleet(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates one or more devices in a fleet.
   */
  updateDevices(params: SageMaker.Types.UpdateDevicesRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates one or more devices in a fleet.
   */
  updateDevices(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates the default settings for new user profiles in the domain.
   */
  updateDomain(params: SageMaker.Types.UpdateDomainRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateDomainResponse) => void): Request<SageMaker.Types.UpdateDomainResponse, AWSError>;
  /**
   * Updates the default settings for new user profiles in the domain.
   */
  updateDomain(callback?: (err: AWSError, data: SageMaker.Types.UpdateDomainResponse) => void): Request<SageMaker.Types.UpdateDomainResponse, AWSError>;
  /**
   * Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see  Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.   You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges. 
   */
  updateEndpoint(params: SageMaker.Types.UpdateEndpointInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateEndpointOutput) => void): Request<SageMaker.Types.UpdateEndpointOutput, AWSError>;
  /**
   * Deploys the EndpointConfig specified in the request to a new fleet of instances. SageMaker shifts endpoint traffic to the new instances with the updated endpoint configuration and then deletes the old instances using the previous EndpointConfig (there is no availability loss). For more information about how to control the update and traffic shifting process, see  Update models in production. When SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.   You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig. If you delete the EndpointConfig of an endpoint that is active or being created or updated you may lose visibility into the instance type the endpoint is using. The endpoint must be deleted in order to stop incurring charges. 
   */
  updateEndpoint(callback?: (err: AWSError, data: SageMaker.Types.UpdateEndpointOutput) => void): Request<SageMaker.Types.UpdateEndpointOutput, AWSError>;
  /**
   * Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. 
   */
  updateEndpointWeightsAndCapacities(params: SageMaker.Types.UpdateEndpointWeightsAndCapacitiesInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateEndpointWeightsAndCapacitiesOutput) => void): Request<SageMaker.Types.UpdateEndpointWeightsAndCapacitiesOutput, AWSError>;
  /**
   * Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API. 
   */
  updateEndpointWeightsAndCapacities(callback?: (err: AWSError, data: SageMaker.Types.UpdateEndpointWeightsAndCapacitiesOutput) => void): Request<SageMaker.Types.UpdateEndpointWeightsAndCapacitiesOutput, AWSError>;
  /**
   * Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
   */
  updateExperiment(params: SageMaker.Types.UpdateExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateExperimentResponse) => void): Request<SageMaker.Types.UpdateExperimentResponse, AWSError>;
  /**
   * Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
   */
  updateExperiment(callback?: (err: AWSError, data: SageMaker.Types.UpdateExperimentResponse) => void): Request<SageMaker.Types.UpdateExperimentResponse, AWSError>;
  /**
   * Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.
   */
  updateFeatureGroup(params: SageMaker.Types.UpdateFeatureGroupRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateFeatureGroupResponse) => void): Request<SageMaker.Types.UpdateFeatureGroupResponse, AWSError>;
  /**
   * Updates the feature group by either adding features or updating the online store configuration. Use one of the following request parameters at a time while using the UpdateFeatureGroup API. You can add features for your feature group using the FeatureAdditions request parameter. Features cannot be removed from a feature group. You can update the online store configuration by using the OnlineStoreConfig request parameter. If a TtlDuration is specified, the default TtlDuration applies for all records added to the feature group after the feature group is updated. If a record level TtlDuration exists from using the PutRecord API, the record level TtlDuration applies to that record instead of the default TtlDuration. To remove the default TtlDuration from an existing feature group, use the UpdateFeatureGroup API and set the TtlDuration Unit and Value to null.
   */
  updateFeatureGroup(callback?: (err: AWSError, data: SageMaker.Types.UpdateFeatureGroupResponse) => void): Request<SageMaker.Types.UpdateFeatureGroupResponse, AWSError>;
  /**
   * Updates the description and parameters of the feature group.
   */
  updateFeatureMetadata(params: SageMaker.Types.UpdateFeatureMetadataRequest, callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Updates the description and parameters of the feature group.
   */
  updateFeatureMetadata(callback?: (err: AWSError, data: {}) => void): Request<{}, AWSError>;
  /**
   * Update a hub.
   */
  updateHub(params: SageMaker.Types.UpdateHubRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateHubResponse) => void): Request<SageMaker.Types.UpdateHubResponse, AWSError>;
  /**
   * Update a hub.
   */
  updateHub(callback?: (err: AWSError, data: SageMaker.Types.UpdateHubResponse) => void): Request<SageMaker.Types.UpdateHubResponse, AWSError>;
  /**
   * Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
   */
  updateImage(params: SageMaker.Types.UpdateImageRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateImageResponse) => void): Request<SageMaker.Types.UpdateImageResponse, AWSError>;
  /**
   * Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
   */
  updateImage(callback?: (err: AWSError, data: SageMaker.Types.UpdateImageResponse) => void): Request<SageMaker.Types.UpdateImageResponse, AWSError>;
  /**
   * Updates the properties of a SageMaker image version.
   */
  updateImageVersion(params: SageMaker.Types.UpdateImageVersionRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateImageVersionResponse) => void): Request<SageMaker.Types.UpdateImageVersionResponse, AWSError>;
  /**
   * Updates the properties of a SageMaker image version.
   */
  updateImageVersion(callback?: (err: AWSError, data: SageMaker.Types.UpdateImageVersionResponse) => void): Request<SageMaker.Types.UpdateImageVersionResponse, AWSError>;
  /**
   * Updates an inference component.
   */
  updateInferenceComponent(params: SageMaker.Types.UpdateInferenceComponentInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceComponentOutput) => void): Request<SageMaker.Types.UpdateInferenceComponentOutput, AWSError>;
  /**
   * Updates an inference component.
   */
  updateInferenceComponent(callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceComponentOutput) => void): Request<SageMaker.Types.UpdateInferenceComponentOutput, AWSError>;
  /**
   * Runtime settings for a model that is deployed with an inference component.
   */
  updateInferenceComponentRuntimeConfig(params: SageMaker.Types.UpdateInferenceComponentRuntimeConfigInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceComponentRuntimeConfigOutput) => void): Request<SageMaker.Types.UpdateInferenceComponentRuntimeConfigOutput, AWSError>;
  /**
   * Runtime settings for a model that is deployed with an inference component.
   */
  updateInferenceComponentRuntimeConfig(callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceComponentRuntimeConfigOutput) => void): Request<SageMaker.Types.UpdateInferenceComponentRuntimeConfigOutput, AWSError>;
  /**
   *  Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment. 
   */
  updateInferenceExperiment(params: SageMaker.Types.UpdateInferenceExperimentRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceExperimentResponse) => void): Request<SageMaker.Types.UpdateInferenceExperimentResponse, AWSError>;
  /**
   *  Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment. 
   */
  updateInferenceExperiment(callback?: (err: AWSError, data: SageMaker.Types.UpdateInferenceExperimentResponse) => void): Request<SageMaker.Types.UpdateInferenceExperimentResponse, AWSError>;
  /**
   * Updates properties of an existing MLflow Tracking Server.
   */
  updateMlflowTrackingServer(params: SageMaker.Types.UpdateMlflowTrackingServerRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateMlflowTrackingServerResponse) => void): Request<SageMaker.Types.UpdateMlflowTrackingServerResponse, AWSError>;
  /**
   * Updates properties of an existing MLflow Tracking Server.
   */
  updateMlflowTrackingServer(callback?: (err: AWSError, data: SageMaker.Types.UpdateMlflowTrackingServerResponse) => void): Request<SageMaker.Types.UpdateMlflowTrackingServerResponse, AWSError>;
  /**
   * Update an Amazon SageMaker Model Card.  You cannot update both model card content and model card status in a single call. 
   */
  updateModelCard(params: SageMaker.Types.UpdateModelCardRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateModelCardResponse) => void): Request<SageMaker.Types.UpdateModelCardResponse, AWSError>;
  /**
   * Update an Amazon SageMaker Model Card.  You cannot update both model card content and model card status in a single call. 
   */
  updateModelCard(callback?: (err: AWSError, data: SageMaker.Types.UpdateModelCardResponse) => void): Request<SageMaker.Types.UpdateModelCardResponse, AWSError>;
  /**
   * Updates a versioned model.
   */
  updateModelPackage(params: SageMaker.Types.UpdateModelPackageInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateModelPackageOutput) => void): Request<SageMaker.Types.UpdateModelPackageOutput, AWSError>;
  /**
   * Updates a versioned model.
   */
  updateModelPackage(callback?: (err: AWSError, data: SageMaker.Types.UpdateModelPackageOutput) => void): Request<SageMaker.Types.UpdateModelPackageOutput, AWSError>;
  /**
   * Update the parameters of a model monitor alert.
   */
  updateMonitoringAlert(params: SageMaker.Types.UpdateMonitoringAlertRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateMonitoringAlertResponse) => void): Request<SageMaker.Types.UpdateMonitoringAlertResponse, AWSError>;
  /**
   * Update the parameters of a model monitor alert.
   */
  updateMonitoringAlert(callback?: (err: AWSError, data: SageMaker.Types.UpdateMonitoringAlertResponse) => void): Request<SageMaker.Types.UpdateMonitoringAlertResponse, AWSError>;
  /**
   * Updates a previously created schedule.
   */
  updateMonitoringSchedule(params: SageMaker.Types.UpdateMonitoringScheduleRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateMonitoringScheduleResponse) => void): Request<SageMaker.Types.UpdateMonitoringScheduleResponse, AWSError>;
  /**
   * Updates a previously created schedule.
   */
  updateMonitoringSchedule(callback?: (err: AWSError, data: SageMaker.Types.UpdateMonitoringScheduleResponse) => void): Request<SageMaker.Types.UpdateMonitoringScheduleResponse, AWSError>;
  /**
   * Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
   */
  updateNotebookInstance(params: SageMaker.Types.UpdateNotebookInstanceInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateNotebookInstanceOutput) => void): Request<SageMaker.Types.UpdateNotebookInstanceOutput, AWSError>;
  /**
   * Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements.
   */
  updateNotebookInstance(callback?: (err: AWSError, data: SageMaker.Types.UpdateNotebookInstanceOutput) => void): Request<SageMaker.Types.UpdateNotebookInstanceOutput, AWSError>;
  /**
   * Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
   */
  updateNotebookInstanceLifecycleConfig(params: SageMaker.Types.UpdateNotebookInstanceLifecycleConfigInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.UpdateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
   */
  updateNotebookInstanceLifecycleConfig(callback?: (err: AWSError, data: SageMaker.Types.UpdateNotebookInstanceLifecycleConfigOutput) => void): Request<SageMaker.Types.UpdateNotebookInstanceLifecycleConfigOutput, AWSError>;
  /**
   * Updates a pipeline.
   */
  updatePipeline(params: SageMaker.Types.UpdatePipelineRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdatePipelineResponse) => void): Request<SageMaker.Types.UpdatePipelineResponse, AWSError>;
  /**
   * Updates a pipeline.
   */
  updatePipeline(callback?: (err: AWSError, data: SageMaker.Types.UpdatePipelineResponse) => void): Request<SageMaker.Types.UpdatePipelineResponse, AWSError>;
  /**
   * Updates a pipeline execution.
   */
  updatePipelineExecution(params: SageMaker.Types.UpdatePipelineExecutionRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdatePipelineExecutionResponse) => void): Request<SageMaker.Types.UpdatePipelineExecutionResponse, AWSError>;
  /**
   * Updates a pipeline execution.
   */
  updatePipelineExecution(callback?: (err: AWSError, data: SageMaker.Types.UpdatePipelineExecutionResponse) => void): Request<SageMaker.Types.UpdatePipelineExecutionResponse, AWSError>;
  /**
   * Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.  You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project. 
   */
  updateProject(params: SageMaker.Types.UpdateProjectInput, callback?: (err: AWSError, data: SageMaker.Types.UpdateProjectOutput) => void): Request<SageMaker.Types.UpdateProjectOutput, AWSError>;
  /**
   * Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.  You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project. 
   */
  updateProject(callback?: (err: AWSError, data: SageMaker.Types.UpdateProjectOutput) => void): Request<SageMaker.Types.UpdateProjectOutput, AWSError>;
  /**
   * Updates the settings of a space.
   */
  updateSpace(params: SageMaker.Types.UpdateSpaceRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateSpaceResponse) => void): Request<SageMaker.Types.UpdateSpaceResponse, AWSError>;
  /**
   * Updates the settings of a space.
   */
  updateSpace(callback?: (err: AWSError, data: SageMaker.Types.UpdateSpaceResponse) => void): Request<SageMaker.Types.UpdateSpaceResponse, AWSError>;
  /**
   * Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
   */
  updateTrainingJob(params: SageMaker.Types.UpdateTrainingJobRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateTrainingJobResponse) => void): Request<SageMaker.Types.UpdateTrainingJobResponse, AWSError>;
  /**
   * Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.
   */
  updateTrainingJob(callback?: (err: AWSError, data: SageMaker.Types.UpdateTrainingJobResponse) => void): Request<SageMaker.Types.UpdateTrainingJobResponse, AWSError>;
  /**
   * Updates the display name of a trial.
   */
  updateTrial(params: SageMaker.Types.UpdateTrialRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateTrialResponse) => void): Request<SageMaker.Types.UpdateTrialResponse, AWSError>;
  /**
   * Updates the display name of a trial.
   */
  updateTrial(callback?: (err: AWSError, data: SageMaker.Types.UpdateTrialResponse) => void): Request<SageMaker.Types.UpdateTrialResponse, AWSError>;
  /**
   * Updates one or more properties of a trial component.
   */
  updateTrialComponent(params: SageMaker.Types.UpdateTrialComponentRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateTrialComponentResponse) => void): Request<SageMaker.Types.UpdateTrialComponentResponse, AWSError>;
  /**
   * Updates one or more properties of a trial component.
   */
  updateTrialComponent(callback?: (err: AWSError, data: SageMaker.Types.UpdateTrialComponentResponse) => void): Request<SageMaker.Types.UpdateTrialComponentResponse, AWSError>;
  /**
   * Updates a user profile.
   */
  updateUserProfile(params: SageMaker.Types.UpdateUserProfileRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateUserProfileResponse) => void): Request<SageMaker.Types.UpdateUserProfileResponse, AWSError>;
  /**
   * Updates a user profile.
   */
  updateUserProfile(callback?: (err: AWSError, data: SageMaker.Types.UpdateUserProfileResponse) => void): Request<SageMaker.Types.UpdateUserProfileResponse, AWSError>;
  /**
   * Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet.  Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "10.0.0.0/16".  Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.  Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.   You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.  After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.  This operation only applies to private workforces. 
   */
  updateWorkforce(params: SageMaker.Types.UpdateWorkforceRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateWorkforceResponse) => void): Request<SageMaker.Types.UpdateWorkforceResponse, AWSError>;
  /**
   * Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration. The worker portal is now supported in VPC and public internet.  Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal. To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "10.0.0.0/16".  Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.  Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.   You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.  After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.  This operation only applies to private workforces. 
   */
  updateWorkforce(callback?: (err: AWSError, data: SageMaker.Types.UpdateWorkforceResponse) => void): Request<SageMaker.Types.UpdateWorkforceResponse, AWSError>;
  /**
   * Updates an existing work team with new member definitions or description.
   */
  updateWorkteam(params: SageMaker.Types.UpdateWorkteamRequest, callback?: (err: AWSError, data: SageMaker.Types.UpdateWorkteamResponse) => void): Request<SageMaker.Types.UpdateWorkteamResponse, AWSError>;
  /**
   * Updates an existing work team with new member definitions or description.
   */
  updateWorkteam(callback?: (err: AWSError, data: SageMaker.Types.UpdateWorkteamResponse) => void): Request<SageMaker.Types.UpdateWorkteamResponse, AWSError>;
  /**
   * Waits for the notebookInstanceInService state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceInService", params: SageMaker.Types.DescribeNotebookInstanceInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the notebookInstanceInService state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceInService", callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the notebookInstanceStopped state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceStopped", params: SageMaker.Types.DescribeNotebookInstanceInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the notebookInstanceStopped state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceStopped", callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the notebookInstanceDeleted state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceDeleted", params: SageMaker.Types.DescribeNotebookInstanceInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the notebookInstanceDeleted state by periodically calling the underlying SageMaker.describeNotebookInstanceoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "notebookInstanceDeleted", callback?: (err: AWSError, data: SageMaker.Types.DescribeNotebookInstanceOutput) => void): Request<SageMaker.Types.DescribeNotebookInstanceOutput, AWSError>;
  /**
   * Waits for the trainingJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeTrainingJoboperation every 120 seconds (at most 180 times).
   */
  waitFor(state: "trainingJobCompletedOrStopped", params: SageMaker.Types.DescribeTrainingJobRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeTrainingJobResponse) => void): Request<SageMaker.Types.DescribeTrainingJobResponse, AWSError>;
  /**
   * Waits for the trainingJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeTrainingJoboperation every 120 seconds (at most 180 times).
   */
  waitFor(state: "trainingJobCompletedOrStopped", callback?: (err: AWSError, data: SageMaker.Types.DescribeTrainingJobResponse) => void): Request<SageMaker.Types.DescribeTrainingJobResponse, AWSError>;
  /**
   * Waits for the endpointInService state by periodically calling the underlying SageMaker.describeEndpointoperation every 30 seconds (at most 120 times).
   */
  waitFor(state: "endpointInService", params: SageMaker.Types.DescribeEndpointInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Waits for the endpointInService state by periodically calling the underlying SageMaker.describeEndpointoperation every 30 seconds (at most 120 times).
   */
  waitFor(state: "endpointInService", callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Waits for the endpointDeleted state by periodically calling the underlying SageMaker.describeEndpointoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "endpointDeleted", params: SageMaker.Types.DescribeEndpointInput & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Waits for the endpointDeleted state by periodically calling the underlying SageMaker.describeEndpointoperation every 30 seconds (at most 60 times).
   */
  waitFor(state: "endpointDeleted", callback?: (err: AWSError, data: SageMaker.Types.DescribeEndpointOutput) => void): Request<SageMaker.Types.DescribeEndpointOutput, AWSError>;
  /**
   * Waits for the transformJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeTransformJoboperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "transformJobCompletedOrStopped", params: SageMaker.Types.DescribeTransformJobRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeTransformJobResponse) => void): Request<SageMaker.Types.DescribeTransformJobResponse, AWSError>;
  /**
   * Waits for the transformJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeTransformJoboperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "transformJobCompletedOrStopped", callback?: (err: AWSError, data: SageMaker.Types.DescribeTransformJobResponse) => void): Request<SageMaker.Types.DescribeTransformJobResponse, AWSError>;
  /**
   * Waits for the processingJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeProcessingJoboperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "processingJobCompletedOrStopped", params: SageMaker.Types.DescribeProcessingJobRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeProcessingJobResponse) => void): Request<SageMaker.Types.DescribeProcessingJobResponse, AWSError>;
  /**
   * Waits for the processingJobCompletedOrStopped state by periodically calling the underlying SageMaker.describeProcessingJoboperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "processingJobCompletedOrStopped", callback?: (err: AWSError, data: SageMaker.Types.DescribeProcessingJobResponse) => void): Request<SageMaker.Types.DescribeProcessingJobResponse, AWSError>;
  /**
   * Waits for the imageCreated state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageCreated", params: SageMaker.Types.DescribeImageRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageCreated state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageCreated", callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageUpdated state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageUpdated", params: SageMaker.Types.DescribeImageRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageUpdated state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageUpdated", callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageDeleted state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageDeleted", params: SageMaker.Types.DescribeImageRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageDeleted state by periodically calling the underlying SageMaker.describeImageoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageDeleted", callback?: (err: AWSError, data: SageMaker.Types.DescribeImageResponse) => void): Request<SageMaker.Types.DescribeImageResponse, AWSError>;
  /**
   * Waits for the imageVersionCreated state by periodically calling the underlying SageMaker.describeImageVersionoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageVersionCreated", params: SageMaker.Types.DescribeImageVersionRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
  /**
   * Waits for the imageVersionCreated state by periodically calling the underlying SageMaker.describeImageVersionoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageVersionCreated", callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
  /**
   * Waits for the imageVersionDeleted state by periodically calling the underlying SageMaker.describeImageVersionoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageVersionDeleted", params: SageMaker.Types.DescribeImageVersionRequest & {$waiter?: WaiterConfiguration}, callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
  /**
   * Waits for the imageVersionDeleted state by periodically calling the underlying SageMaker.describeImageVersionoperation every 60 seconds (at most 60 times).
   */
  waitFor(state: "imageVersionDeleted", callback?: (err: AWSError, data: SageMaker.Types.DescribeImageVersionResponse) => void): Request<SageMaker.Types.DescribeImageVersionResponse, AWSError>;
}
declare namespace SageMaker {
  export type Accept = string;
  export type AcceptEula = boolean;
  export type AccountId = string;
  export type ActionArn = string;
  export interface ActionSource {
    /**
     * The URI of the source.
     */
    SourceUri: SourceUri;
    /**
     * The type of the source.
     */
    SourceType?: String256;
    /**
     * The ID of the source.
     */
    SourceId?: String256;
  }
  export type ActionStatus = "Unknown"|"InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export type ActionSummaries = ActionSummary[];
  export interface ActionSummary {
    /**
     * The Amazon Resource Name (ARN) of the action.
     */
    ActionArn?: ActionArn;
    /**
     * The name of the action.
     */
    ActionName?: ExperimentEntityName;
    /**
     * The source of the action.
     */
    Source?: ActionSource;
    /**
     * The type of the action.
     */
    ActionType?: String64;
    /**
     * The status of the action.
     */
    Status?: ActionStatus;
    /**
     * When the action was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the action was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export interface AddAssociationRequest {
    /**
     * The ARN of the source.
     */
    SourceArn: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination.
     */
    DestinationArn: AssociationEntityArn;
    /**
     * The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.   ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.   AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.   DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.   Produced - The source generated the destination. For example, a training job produced a model artifact.  
     */
    AssociationType?: AssociationEdgeType;
  }
  export interface AddAssociationResponse {
    /**
     * The ARN of the source.
     */
    SourceArn?: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination.
     */
    DestinationArn?: AssociationEntityArn;
  }
  export interface AddTagsInput {
    /**
     * The Amazon Resource Name (ARN) of the resource that you want to tag.
     */
    ResourceArn: ResourceArn;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags: TagList;
  }
  export interface AddTagsOutput {
    /**
     * A list of tags associated with the SageMaker resource.
     */
    Tags?: TagList;
  }
  export type AdditionalCodeRepositoryNamesOrUrls = CodeRepositoryNameOrUrl[];
  export interface AdditionalInferenceSpecificationDefinition {
    /**
     * A unique name to identify the additional inference specification. The name must be unique within the list of your additional inference specifications for a particular model package.
     */
    Name: EntityName;
    /**
     * A description of the additional Inference specification
     */
    Description?: EntityDescription;
    /**
     * The Amazon ECR registry path of the Docker image that contains the inference code.
     */
    Containers: ModelPackageContainerDefinitionList;
    /**
     * A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed.
     */
    SupportedTransformInstanceTypes?: TransformInstanceTypes;
    /**
     * A list of the instance types that are used to generate inferences in real-time.
     */
    SupportedRealtimeInferenceInstanceTypes?: RealtimeInferenceInstanceTypes;
    /**
     * The supported MIME types for the input data.
     */
    SupportedContentTypes?: ContentTypes;
    /**
     * The supported MIME types for the output data.
     */
    SupportedResponseMIMETypes?: ResponseMIMETypes;
  }
  export type AdditionalInferenceSpecifications = AdditionalInferenceSpecificationDefinition[];
  export type AdditionalModelChannelName = string;
  export interface AdditionalModelDataSource {
    /**
     * A custom name for this AdditionalModelDataSource object.
     */
    ChannelName: AdditionalModelChannelName;
    S3DataSource: S3ModelDataSource;
  }
  export type AdditionalModelDataSources = AdditionalModelDataSource[];
  export interface AdditionalS3DataSource {
    /**
     * The data type of the additional data source that you specify for use in inference or training. 
     */
    S3DataType: AdditionalS3DataSourceDataType;
    /**
     * The uniform resource identifier (URI) used to identify an additional data source used in inference or training.
     */
    S3Uri: S3Uri;
    /**
     * The type of compression used for an additional data source used in inference or training. Specify None if your additional data source is not compressed.
     */
    CompressionType?: CompressionType;
  }
  export type AdditionalS3DataSourceDataType = "S3Object"|"S3Prefix"|string;
  export interface AgentVersion {
    /**
     * Version of the agent.
     */
    Version: EdgeVersion;
    /**
     * The number of Edge Manager agents.
     */
    AgentCount: Long;
  }
  export type AgentVersions = AgentVersion[];
  export type AggregationTransformationValue = "sum"|"avg"|"first"|"min"|"max"|string;
  export type AggregationTransformations = {[key: string]: AggregationTransformationValue};
  export interface Alarm {
    /**
     * The name of a CloudWatch alarm in your account.
     */
    AlarmName?: AlarmName;
  }
  export type AlarmList = Alarm[];
  export type AlarmName = string;
  export type AlgorithmArn = string;
  export type AlgorithmImage = string;
  export type AlgorithmSortBy = "Name"|"CreationTime"|string;
  export interface AlgorithmSpecification {
    /**
     * The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for SageMaker built-in algorithms, see Docker Registry Paths and Example Code in the Amazon SageMaker developer guide. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information about using your custom training container, see Using Your Own Algorithms with Amazon SageMaker.  You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. For more information, see the note in the AlgorithmName parameter description. 
     */
    TrainingImage?: AlgorithmImage;
    /**
     * The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.  You must specify either the algorithm name to the AlgorithmName parameter or the image URI of the algorithm container to the TrainingImage parameter. Note that the AlgorithmName parameter is mutually exclusive with the TrainingImage parameter. If you specify a value for the AlgorithmName parameter, you can't specify a value for TrainingImage, and vice versa. If you specify values for both parameters, the training job might break; if you don't specify any value for both parameters, the training job might raise a null error. 
     */
    AlgorithmName?: ArnOrName;
    TrainingInputMode: TrainingInputMode;
    /**
     * A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
     */
    MetricDefinitions?: MetricDefinitionList;
    /**
     * To generate and save time-series metrics during training, set to true. The default is false and time-series metrics aren't generated except in the following cases:   You use one of the SageMaker built-in algorithms   You use one of the following Prebuilt SageMaker Docker Images:   Tensorflow (version &gt;= 1.15)   MXNet (version &gt;= 1.6)   PyTorch (version &gt;= 1.3)     You specify at least one MetricDefinition   
     */
    EnableSageMakerMetricsTimeSeries?: Boolean;
    /**
     * The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
     */
    ContainerEntrypoint?: TrainingContainerEntrypoint;
    /**
     * The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
     */
    ContainerArguments?: TrainingContainerArguments;
    /**
     * The configuration to use an image from a private Docker registry for a training job.
     */
    TrainingImageConfig?: TrainingImageConfig;
  }
  export type AlgorithmStatus = "Pending"|"InProgress"|"Completed"|"Failed"|"Deleting"|string;
  export interface AlgorithmStatusDetails {
    /**
     * The status of algorithm validation.
     */
    ValidationStatuses?: AlgorithmStatusItemList;
    /**
     * The status of the scan of the algorithm's Docker image container.
     */
    ImageScanStatuses?: AlgorithmStatusItemList;
  }
  export interface AlgorithmStatusItem {
    /**
     * The name of the algorithm for which the overall status is being reported.
     */
    Name: EntityName;
    /**
     * The current status.
     */
    Status: DetailedAlgorithmStatus;
    /**
     * if the overall status is Failed, the reason for the failure.
     */
    FailureReason?: String;
  }
  export type AlgorithmStatusItemList = AlgorithmStatusItem[];
  export interface AlgorithmSummary {
    /**
     * The name of the algorithm that is described by the summary.
     */
    AlgorithmName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the algorithm.
     */
    AlgorithmArn: AlgorithmArn;
    /**
     * A brief description of the algorithm.
     */
    AlgorithmDescription?: EntityDescription;
    /**
     * A timestamp that shows when the algorithm was created.
     */
    CreationTime: CreationTime;
    /**
     * The overall status of the algorithm.
     */
    AlgorithmStatus: AlgorithmStatus;
  }
  export type AlgorithmSummaryList = AlgorithmSummary[];
  export interface AlgorithmValidationProfile {
    /**
     * The name of the profile for the algorithm. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
     */
    ProfileName: EntityName;
    /**
     * The TrainingJobDefinition object that describes the training job that SageMaker runs to validate your algorithm.
     */
    TrainingJobDefinition: TrainingJobDefinition;
    /**
     * The TransformJobDefinition object that describes the transform job that SageMaker runs to validate your algorithm.
     */
    TransformJobDefinition?: TransformJobDefinition;
  }
  export type AlgorithmValidationProfiles = AlgorithmValidationProfile[];
  export interface AlgorithmValidationSpecification {
    /**
     * The IAM roles that SageMaker uses to run the training jobs.
     */
    ValidationRole: RoleArn;
    /**
     * An array of AlgorithmValidationProfile objects, each of which specifies a training job and batch transform job that SageMaker runs to validate your algorithm.
     */
    ValidationProfiles: AlgorithmValidationProfiles;
  }
  export interface AmazonQSettings {
    /**
     * Whether Amazon Q has been enabled within the domain.
     */
    Status?: FeatureStatus;
    /**
     * The ARN of the Amazon Q profile used within the domain.
     */
    QProfileArn?: QProfileArn;
  }
  export interface AnnotationConsolidationConfig {
    /**
     * The Amazon Resource Name (ARN) of a Lambda function implements the logic for annotation consolidation and to process output data. This parameter is required for all labeling jobs. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for AnnotationConsolidationLambdaArn. For custom labeling workflows, see Post-annotation Lambda.   Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.    arn:aws:lambda:us-east-1:432418664414:function:ACS-BoundingBox     arn:aws:lambda:us-east-2:266458841044:function:ACS-BoundingBox     arn:aws:lambda:us-west-2:081040173940:function:ACS-BoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:ACS-BoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-BoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-BoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:ACS-BoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:ACS-BoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-BoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:ACS-BoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-BoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:ACS-BoundingBox     Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClass     arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClass     arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClass     Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:us-east-2:266458841044:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:us-west-2:081040173940:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:eu-west-1:568282634449:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ap-south-1:565803892007:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:eu-central-1:203001061592:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:eu-west-2:487402164563:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-ImageMultiClassMultiLabel     arn:aws:lambda:ca-central-1:918755190332:function:ACS-ImageMultiClassMultiLabel     Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.    arn:aws:lambda:us-east-1:432418664414:function:ACS-SemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:ACS-SemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:ACS-SemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:ACS-SemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-SemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-SemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:ACS-SemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:ACS-SemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-SemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:ACS-SemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-SemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:ACS-SemanticSegmentation     Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClass     arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClass     arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClass     Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:us-east-2:266458841044:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:us-west-2:081040173940:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:eu-west-1:568282634449:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ap-south-1:565803892007:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:eu-central-1:203001061592:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:eu-west-2:487402164563:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-TextMultiClassMultiLabel     arn:aws:lambda:ca-central-1:918755190332:function:ACS-TextMultiClassMultiLabel     Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.    arn:aws:lambda:us-east-1:432418664414:function:ACS-NamedEntityRecognition     arn:aws:lambda:us-east-2:266458841044:function:ACS-NamedEntityRecognition     arn:aws:lambda:us-west-2:081040173940:function:ACS-NamedEntityRecognition     arn:aws:lambda:eu-west-1:568282634449:function:ACS-NamedEntityRecognition     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-NamedEntityRecognition     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-NamedEntityRecognition     arn:aws:lambda:ap-south-1:565803892007:function:ACS-NamedEntityRecognition     arn:aws:lambda:eu-central-1:203001061592:function:ACS-NamedEntityRecognition     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-NamedEntityRecognition     arn:aws:lambda:eu-west-2:487402164563:function:ACS-NamedEntityRecognition     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-NamedEntityRecognition     arn:aws:lambda:ca-central-1:918755190332:function:ACS-NamedEntityRecognition     Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.    arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoMultiClass     arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoMultiClass     arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoMultiClass     Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.    arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectDetection     Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.     arn:aws:lambda:us-east-1:432418664414:function:ACS-VideoObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:ACS-VideoObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:ACS-VideoObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:ACS-VideoObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VideoObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VideoObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:ACS-VideoObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:ACS-VideoObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VideoObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:ACS-VideoObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VideoObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:ACS-VideoObjectTracking     3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.    arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectDetection     3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.     arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudObjectTracking     3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.    arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:ACS-3DPointCloudSemanticSegmentation     Use the following ARNs for Label Verification and Adjustment Jobs  Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .  Semantic Segmentation Adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.    arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentSemanticSegmentation     Semantic Segmentation Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationSemanticSegmentation     Bounding Box Adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.    arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentBoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentBoundingBox     Bounding Box Verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:ACS-VerificationBoundingBox     arn:aws:lambda:us-east-2:266458841044:function:ACS-VerificationBoundingBox     arn:aws:lambda:us-west-2:081040173940:function:ACS-VerificationBoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:ACS-VerificationBoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-VerificationBoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-VerificationBoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:ACS-VerificationBoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:ACS-VerificationBoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-VerificationBoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:ACS-VerificationBoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-VerificationBoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:ACS-VerificationBoundingBox     Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.    arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectDetection     Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.    arn:aws:lambda:us-east-1:432418664414:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-AdjustmentVideoObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:ACS-AdjustmentVideoObjectTracking     3D Point Cloud Object Detection Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects in a 3D point cloud.     arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectDetection     3D Point Cloud Object Tracking Adjustment - Use this task type when you want workers to adjust 3D cuboids around objects that appear in a sequence of 3D point cloud frames.    arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudObjectTracking     3D Point Cloud Semantic Segmentation Adjustment - Use this task type when you want workers to adjust a point-level semantic segmentation masks using a paint tool.    arn:aws:lambda:us-east-1:432418664414:function:ACS-3DPointCloudSemanticSegmentation     arn:aws:lambda:us-east-1:432418664414:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:ACS-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:ACS-Adjustment3DPointCloudSemanticSegmentation   
     */
    AnnotationConsolidationLambdaArn: LambdaFunctionArn;
  }
  export type AppArn = string;
  export interface AppDetails {
    /**
     * The domain ID.
     */
    DomainId?: DomainId;
    /**
     * The user profile name.
     */
    UserProfileName?: UserProfileName;
    /**
     * The name of the space.
     */
    SpaceName?: SpaceName;
    /**
     * The type of app.
     */
    AppType?: AppType;
    /**
     * The name of the app.
     */
    AppName?: AppName;
    /**
     * The status.
     */
    Status?: AppStatus;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    ResourceSpec?: ResourceSpec;
  }
  export type AppImageConfigArn = string;
  export interface AppImageConfigDetails {
    /**
     * The ARN of the AppImageConfig.
     */
    AppImageConfigArn?: AppImageConfigArn;
    /**
     * The name of the AppImageConfig. Must be unique to your account.
     */
    AppImageConfigName?: AppImageConfigName;
    /**
     * When the AppImageConfig was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the AppImageConfig was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The configuration for the file system and kernels in the SageMaker image.
     */
    KernelGatewayImageConfig?: KernelGatewayImageConfig;
    /**
     * The configuration for the file system and the runtime, such as the environment variables and entry point.
     */
    JupyterLabAppImageConfig?: JupyterLabAppImageConfig;
    /**
     * The configuration for the file system and the runtime, such as the environment variables and entry point.
     */
    CodeEditorAppImageConfig?: CodeEditorAppImageConfig;
  }
  export type AppImageConfigList = AppImageConfigDetails[];
  export type AppImageConfigName = string;
  export type AppImageConfigSortKey = "CreationTime"|"LastModifiedTime"|"Name"|string;
  export type AppInstanceType = "system"|"ml.t3.micro"|"ml.t3.small"|"ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.8xlarge"|"ml.m5.12xlarge"|"ml.m5.16xlarge"|"ml.m5.24xlarge"|"ml.m5d.large"|"ml.m5d.xlarge"|"ml.m5d.2xlarge"|"ml.m5d.4xlarge"|"ml.m5d.8xlarge"|"ml.m5d.12xlarge"|"ml.m5d.16xlarge"|"ml.m5d.24xlarge"|"ml.c5.large"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.12xlarge"|"ml.c5.18xlarge"|"ml.c5.24xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.p3dn.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.g6.xlarge"|"ml.g6.2xlarge"|"ml.g6.4xlarge"|"ml.g6.8xlarge"|"ml.g6.12xlarge"|"ml.g6.16xlarge"|"ml.g6.24xlarge"|"ml.g6.48xlarge"|"ml.geospatial.interactive"|"ml.p4d.24xlarge"|"ml.p4de.24xlarge"|"ml.trn1.2xlarge"|"ml.trn1.32xlarge"|"ml.trn1n.32xlarge"|"ml.p5.48xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c6i.large"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r6i.large"|"ml.r6i.xlarge"|"ml.r6i.2xlarge"|"ml.r6i.4xlarge"|"ml.r6i.8xlarge"|"ml.r6i.12xlarge"|"ml.r6i.16xlarge"|"ml.r6i.24xlarge"|"ml.r6i.32xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|"ml.m6id.large"|"ml.m6id.xlarge"|"ml.m6id.2xlarge"|"ml.m6id.4xlarge"|"ml.m6id.8xlarge"|"ml.m6id.12xlarge"|"ml.m6id.16xlarge"|"ml.m6id.24xlarge"|"ml.m6id.32xlarge"|"ml.c6id.large"|"ml.c6id.xlarge"|"ml.c6id.2xlarge"|"ml.c6id.4xlarge"|"ml.c6id.8xlarge"|"ml.c6id.12xlarge"|"ml.c6id.16xlarge"|"ml.c6id.24xlarge"|"ml.c6id.32xlarge"|"ml.r6id.large"|"ml.r6id.xlarge"|"ml.r6id.2xlarge"|"ml.r6id.4xlarge"|"ml.r6id.8xlarge"|"ml.r6id.12xlarge"|"ml.r6id.16xlarge"|"ml.r6id.24xlarge"|"ml.r6id.32xlarge"|string;
  export interface AppLifecycleManagement {
    /**
     * Settings related to idle shutdown of Studio applications.
     */
    IdleSettings?: IdleSettings;
  }
  export type AppList = AppDetails[];
  export type AppManaged = boolean;
  export type AppName = string;
  export type AppNetworkAccessType = "PublicInternetOnly"|"VpcOnly"|string;
  export type AppSecurityGroupManagement = "Service"|"Customer"|string;
  export type AppSortKey = "CreationTime"|string;
  export interface AppSpecification {
    /**
     * The container image to be run by the processing job.
     */
    ImageUri: ImageUri;
    /**
     * The entrypoint for a container used to run a processing job.
     */
    ContainerEntrypoint?: ContainerEntrypoint;
    /**
     * The arguments for a container used to run a processing job.
     */
    ContainerArguments?: ContainerArguments;
  }
  export type AppStatus = "Deleted"|"Deleting"|"Failed"|"InService"|"Pending"|string;
  export type AppType = "JupyterServer"|"KernelGateway"|"DetailedProfiler"|"TensorBoard"|"CodeEditor"|"JupyterLab"|"RStudioServerPro"|"RSessionGateway"|"Canvas"|string;
  export type ApprovalDescription = string;
  export type ArnOrName = string;
  export type ArtifactArn = string;
  export type ArtifactDigest = string;
  export type ArtifactProperties = {[key: string]: ArtifactPropertyValue};
  export type ArtifactPropertyValue = string;
  export interface ArtifactSource {
    /**
     * The URI of the source.
     */
    SourceUri: SourceUri;
    /**
     * A list of source types.
     */
    SourceTypes?: ArtifactSourceTypes;
  }
  export type ArtifactSourceIdType = "MD5Hash"|"S3ETag"|"S3Version"|"Custom"|string;
  export interface ArtifactSourceType {
    /**
     * The type of ID.
     */
    SourceIdType: ArtifactSourceIdType;
    /**
     * The ID.
     */
    Value: String256;
  }
  export type ArtifactSourceTypes = ArtifactSourceType[];
  export type ArtifactSummaries = ArtifactSummary[];
  export interface ArtifactSummary {
    /**
     * The Amazon Resource Name (ARN) of the artifact.
     */
    ArtifactArn?: ArtifactArn;
    /**
     * The name of the artifact.
     */
    ArtifactName?: ExperimentEntityName;
    /**
     * The source of the artifact.
     */
    Source?: ArtifactSource;
    /**
     * The type of the artifact.
     */
    ArtifactType?: String256;
    /**
     * When the artifact was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the artifact was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type AssemblyType = "None"|"Line"|string;
  export interface AssociateTrialComponentRequest {
    /**
     * The name of the component to associated with the trial.
     */
    TrialComponentName: ExperimentEntityName;
    /**
     * The name of the trial to associate with.
     */
    TrialName: ExperimentEntityName;
  }
  export interface AssociateTrialComponentResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
  }
  export type AssociationEdgeType = "ContributedTo"|"AssociatedWith"|"DerivedFrom"|"Produced"|"SameAs"|string;
  export type AssociationEntityArn = string;
  export type AssociationSummaries = AssociationSummary[];
  export interface AssociationSummary {
    /**
     * The ARN of the source.
     */
    SourceArn?: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination.
     */
    DestinationArn?: AssociationEntityArn;
    /**
     * The source type.
     */
    SourceType?: String256;
    /**
     * The destination type.
     */
    DestinationType?: String256;
    /**
     * The type of the association.
     */
    AssociationType?: AssociationEdgeType;
    /**
     * The name of the source.
     */
    SourceName?: ExperimentEntityName;
    /**
     * The name of the destination.
     */
    DestinationName?: ExperimentEntityName;
    /**
     * When the association was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
  }
  export type AssumableRoleArns = RoleArn[];
  export interface AsyncInferenceClientConfig {
    /**
     * The maximum number of concurrent requests sent by the SageMaker client to the model container. If no value is provided, SageMaker chooses an optimal value.
     */
    MaxConcurrentInvocationsPerInstance?: MaxConcurrentInvocationsPerInstance;
  }
  export interface AsyncInferenceConfig {
    /**
     * Configures the behavior of the client used by SageMaker to interact with the model container during asynchronous inference.
     */
    ClientConfig?: AsyncInferenceClientConfig;
    /**
     * Specifies the configuration for asynchronous inference invocation outputs.
     */
    OutputConfig: AsyncInferenceOutputConfig;
  }
  export interface AsyncInferenceNotificationConfig {
    /**
     * Amazon SNS topic to post a notification to when inference completes successfully. If no topic is provided, no notification is sent on success.
     */
    SuccessTopic?: SnsTopicArn;
    /**
     * Amazon SNS topic to post a notification to when inference fails. If no topic is provided, no notification is sent on failure.
     */
    ErrorTopic?: SnsTopicArn;
    /**
     * The Amazon SNS topics where you want the inference response to be included.  The inference response is included only if the response size is less than or equal to 128 KB. 
     */
    IncludeInferenceResponseIn?: AsyncNotificationTopicTypeList;
  }
  export interface AsyncInferenceOutputConfig {
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the asynchronous inference output in Amazon S3. 
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The Amazon S3 location to upload inference responses to.
     */
    S3OutputPath?: DestinationS3Uri;
    /**
     * Specifies the configuration for notifications of inference results for asynchronous inference.
     */
    NotificationConfig?: AsyncInferenceNotificationConfig;
    /**
     * The Amazon S3 location to upload failure inference responses to.
     */
    S3FailurePath?: DestinationS3Uri;
  }
  export type AsyncNotificationTopicTypeList = AsyncNotificationTopicTypes[];
  export type AsyncNotificationTopicTypes = "SUCCESS_NOTIFICATION_TOPIC"|"ERROR_NOTIFICATION_TOPIC"|string;
  export type AthenaCatalog = string;
  export type AthenaDatabase = string;
  export interface AthenaDatasetDefinition {
    Catalog: AthenaCatalog;
    Database: AthenaDatabase;
    QueryString: AthenaQueryString;
    WorkGroup?: AthenaWorkGroup;
    /**
     * The location in Amazon S3 where Athena query results are stored.
     */
    OutputS3Uri: S3Uri;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data generated from an Athena query execution.
     */
    KmsKeyId?: KmsKeyId;
    OutputFormat: AthenaResultFormat;
    OutputCompression?: AthenaResultCompressionType;
  }
  export type AthenaQueryString = string;
  export type AthenaResultCompressionType = "GZIP"|"SNAPPY"|"ZLIB"|string;
  export type AthenaResultFormat = "PARQUET"|"ORC"|"AVRO"|"JSON"|"TEXTFILE"|string;
  export type AthenaWorkGroup = string;
  export type AttributeName = string;
  export type AttributeNames = AttributeName[];
  export type AuthMode = "SSO"|"IAM"|string;
  export type AuthenticationRequestExtraParams = {[key: string]: AuthenticationRequestExtraParamsValue};
  export type AuthenticationRequestExtraParamsKey = string;
  export type AuthenticationRequestExtraParamsValue = string;
  export type AutoGenerateEndpointName = boolean;
  export type AutoMLAlgorithm = "xgboost"|"linear-learner"|"mlp"|"lightgbm"|"catboost"|"randomforest"|"extra-trees"|"nn-torch"|"fastai"|"cnn-qr"|"deepar"|"prophet"|"npts"|"arima"|"ets"|string;
  export interface AutoMLAlgorithmConfig {
    /**
     * The selection of algorithms trained on your dataset to generate the model candidates for an Autopilot job.    For the tabular problem type TabularJobConfig:   Selected algorithms must belong to the list corresponding to the training mode set in AutoMLJobConfig.Mode (ENSEMBLING or HYPERPARAMETER_TUNING). Choose a minimum of 1 algorithm.    In ENSEMBLING mode:   "catboost"   "extra-trees"   "fastai"   "lightgbm"   "linear-learner"   "nn-torch"   "randomforest"   "xgboost"     In HYPERPARAMETER_TUNING mode:   "linear-learner"   "mlp"   "xgboost"        For the time-series forecasting problem type TimeSeriesForecastingJobConfig:    Choose your algorithms from this list.   "cnn-qr"   "deepar"   "prophet"   "arima"   "npts"   "ets"      
     */
    AutoMLAlgorithms: AutoMLAlgorithms;
  }
  export type AutoMLAlgorithms = AutoMLAlgorithm[];
  export type AutoMLAlgorithmsConfig = AutoMLAlgorithmConfig[];
  export interface AutoMLCandidate {
    /**
     * The name of the candidate.
     */
    CandidateName: CandidateName;
    FinalAutoMLJobObjectiveMetric?: FinalAutoMLJobObjectiveMetric;
    /**
     * The objective's status.
     */
    ObjectiveStatus: ObjectiveStatus;
    /**
     * Information about the candidate's steps.
     */
    CandidateSteps: CandidateSteps;
    /**
     * The candidate's status.
     */
    CandidateStatus: CandidateStatus;
    /**
     * Information about the recommended inference container definitions.
     */
    InferenceContainers?: AutoMLContainerDefinitions;
    /**
     * The creation time.
     */
    CreationTime: Timestamp;
    /**
     * The end time.
     */
    EndTime?: Timestamp;
    /**
     * The last modified time.
     */
    LastModifiedTime: Timestamp;
    /**
     * The failure reason.
     */
    FailureReason?: AutoMLFailureReason;
    /**
     * The properties of an AutoML candidate job.
     */
    CandidateProperties?: CandidateProperties;
    /**
     * The mapping of all supported processing unit (CPU, GPU, etc...) to inference container definitions for the candidate. This field is populated for the AutoML jobs V2 (for example, for jobs created by calling CreateAutoMLJobV2) related to image or text classification problem types only.
     */
    InferenceContainerDefinitions?: AutoMLInferenceContainerDefinitions;
  }
  export interface AutoMLCandidateGenerationConfig {
    /**
     * A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job. You can input FeatureAttributeNames (optional) in JSON format as shown below:   { "FeatureAttributeNames":["col1", "col2", ...] }. You can also specify the data type of the feature (optional) in the format shown below:  { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }   These column keys may not include the target column.  In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence. If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.  If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.  The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
     */
    FeatureSpecificationS3Uri?: S3Uri;
    /**
     * Stores the configuration information for the selection of algorithms trained on tabular data. The list of available algorithms to choose from depends on the training mode set in  TabularJobConfig.Mode .    AlgorithmsConfig should not be set if the training mode is set on AUTO.   When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.   When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.   For the list of all algorithms per problem type and training mode, see  AutoMLAlgorithmConfig. For more information on each algorithm, see the Algorithm support section in Autopilot developer guide.
     */
    AlgorithmsConfig?: AutoMLAlgorithmsConfig;
  }
  export interface AutoMLCandidateStep {
    /**
     * Whether the candidate is at the transform, training, or processing step.
     */
    CandidateStepType: CandidateStepType;
    /**
     * The ARN for the candidate's step.
     */
    CandidateStepArn: CandidateStepArn;
    /**
     * The name for the candidate's step.
     */
    CandidateStepName: CandidateStepName;
  }
  export type AutoMLCandidates = AutoMLCandidate[];
  export interface AutoMLChannel {
    /**
     * The data source for an AutoML channel.
     */
    DataSource?: AutoMLDataSource;
    /**
     * You can use Gzip or None. The default value is None.
     */
    CompressionType?: CompressionType;
    /**
     * The name of the target variable in supervised learning, usually represented by 'y'.
     */
    TargetAttributeName: TargetAttributeName;
    /**
     * The content type of the data from the input source. You can use text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.
     */
    ContentType?: ContentType;
    /**
     * The channel type (optional) is an enum string. The default value is training. Channels for training and validation must share the same ContentType and TargetAttributeName. For information on specifying training and validation channel types, see How to specify training and validation datasets.
     */
    ChannelType?: AutoMLChannelType;
    /**
     * If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation. Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in Ensembling mode only.
     */
    SampleWeightAttributeName?: SampleWeightAttributeName;
  }
  export type AutoMLChannelType = "training"|"validation"|string;
  export interface AutoMLComputeConfig {
    /**
     * The configuration for using  EMR Serverless to run the AutoML job V2. To allow your AutoML job V2 to automatically initiate a remote job on EMR Serverless when additional compute resources are needed to process large datasets, you need to provide an EmrServerlessComputeConfig object, which includes an ExecutionRoleARN attribute, to the AutoMLComputeConfig of the AutoML job V2 input request. By seamlessly transitioning to EMR Serverless when required, the AutoML job can handle datasets that would otherwise exceed the initially provisioned resources, without any manual intervention from you.  EMR Serverless is available for the tabular and time series problem types. We recommend setting up this option for tabular datasets larger than 5 GB and time series datasets larger than 30 GB.
     */
    EmrServerlessComputeConfig?: EmrServerlessComputeConfig;
  }
  export interface AutoMLContainerDefinition {
    /**
     * The Amazon Elastic Container Registry (Amazon ECR) path of the container. For more information, see  ContainerDefinition.
     */
    Image: ContainerImage;
    /**
     * The location of the model artifacts. For more information, see  ContainerDefinition.
     */
    ModelDataUrl: Url;
    /**
     * The environment variables to set in the container. For more information, see  ContainerDefinition.
     */
    Environment?: EnvironmentMap;
  }
  export type AutoMLContainerDefinitions = AutoMLContainerDefinition[];
  export interface AutoMLDataSource {
    /**
     * The Amazon S3 location of the input data.
     */
    S3DataSource: AutoMLS3DataSource;
  }
  export interface AutoMLDataSplitConfig {
    /**
     * The validation fraction (optional) is a float that specifies the portion of the training dataset to be used for validation. The default value is 0.2, and values must be greater than 0 and less than 1. We recommend setting this value to be less than 0.5.
     */
    ValidationFraction?: ValidationFraction;
  }
  export type AutoMLFailureReason = string;
  export type AutoMLInferenceContainerDefinitions = {[key: string]: AutoMLContainerDefinitions};
  export type AutoMLInputDataConfig = AutoMLChannel[];
  export type AutoMLJobArn = string;
  export interface AutoMLJobArtifacts {
    /**
     * The URL of the notebook location.
     */
    CandidateDefinitionNotebookLocation?: CandidateDefinitionNotebookLocation;
    /**
     * The URL of the notebook location.
     */
    DataExplorationNotebookLocation?: DataExplorationNotebookLocation;
  }
  export interface AutoMLJobChannel {
    /**
     * The type of channel. Defines whether the data are used for training or validation. The default value is training. Channels for training and validation must share the same ContentType   The type of channel defaults to training for the time-series forecasting problem type. 
     */
    ChannelType?: AutoMLChannelType;
    /**
     * The content type of the data from the input source. The following are the allowed content types for different problems:   For tabular problem types: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.   For image classification: image/png, image/jpeg, or image/*. The default value is image/*.   For text classification: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.   For time-series forecasting: text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.   For text generation (LLMs fine-tuning): text/csv;header=present or x-application/vnd.amazon+parquet. The default value is text/csv;header=present.  
     */
    ContentType?: ContentType;
    /**
     * The allowed compression types depend on the input format and problem type. We allow the compression type Gzip for S3Prefix inputs on tabular data only. For all other inputs, the compression type should be None. If no compression type is provided, we default to None.
     */
    CompressionType?: CompressionType;
    /**
     * The data source for an AutoML channel (Required).
     */
    DataSource?: AutoMLDataSource;
  }
  export interface AutoMLJobCompletionCriteria {
    /**
     * The maximum number of times a training job is allowed to run. For text and image classification, time-series forecasting, as well as text generation (LLMs fine-tuning) problem types, the supported value is 1. For tabular problem types, the maximum value is 750.
     */
    MaxCandidates?: MaxCandidates;
    /**
     * The maximum time, in seconds, that each training job executed inside hyperparameter tuning is allowed to run as part of a hyperparameter tuning job. For more information, see the StoppingCondition used by the CreateHyperParameterTuningJob action. For job V2s (jobs created by calling CreateAutoMLJobV2), this field controls the runtime of the job candidate. For TextGenerationJobConfig problem types, the maximum time defaults to 72 hours (259200 seconds).
     */
    MaxRuntimePerTrainingJobInSeconds?: MaxRuntimePerTrainingJobInSeconds;
    /**
     * The maximum runtime, in seconds, an AutoML job has to complete. If an AutoML job exceeds the maximum runtime, the job is stopped automatically and its processing is ended gracefully. The AutoML job identifies the best model whose training was completed and marks it as the best-performing model. Any unfinished steps of the job, such as automatic one-click Autopilot model deployment, are not completed.
     */
    MaxAutoMLJobRuntimeInSeconds?: MaxAutoMLJobRuntimeInSeconds;
  }
  export interface AutoMLJobConfig {
    /**
     * How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
     */
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * The security configuration for traffic encryption or Amazon VPC settings.
     */
    SecurityConfig?: AutoMLSecurityConfig;
    /**
     * The configuration for generating a candidate for an AutoML job (optional). 
     */
    CandidateGenerationConfig?: AutoMLCandidateGenerationConfig;
    /**
     * The configuration for splitting the input training dataset. Type: AutoMLDataSplitConfig
     */
    DataSplitConfig?: AutoMLDataSplitConfig;
    /**
     * The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones. The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode. The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
     */
    Mode?: AutoMLMode;
  }
  export type AutoMLJobInputDataConfig = AutoMLJobChannel[];
  export type AutoMLJobName = string;
  export interface AutoMLJobObjective {
    /**
     * The name of the objective metric used to measure the predictive quality of a machine learning system. During training, the model's parameters are updated iteratively to optimize its performance based on the feedback provided by the objective metric when evaluating the model on the validation dataset. The list of available metrics supported by Autopilot and the default metric applied when you do not specify a metric name explicitly depend on the problem type.   For tabular problem types:   List of available metrics:     Regression: MAE, MSE, R2, RMSE     Binary classification: Accuracy, AUC, BalancedAccuracy, F1, Precision, Recall     Multiclass classification: Accuracy, BalancedAccuracy, F1macro, PrecisionMacro, RecallMacro    For a description of each metric, see Autopilot metrics for classification and regression.   Default objective metrics:   Regression: MSE.   Binary classification: F1.   Multiclass classification: Accuracy.       For image or text classification problem types:   List of available metrics: Accuracy  For a description of each metric, see Autopilot metrics for text and image classification.   Default objective metrics: Accuracy      For time-series forecasting problem types:   List of available metrics: RMSE, wQL, Average wQL, MASE, MAPE, WAPE  For a description of each metric, see Autopilot metrics for time-series forecasting.   Default objective metrics: AverageWeightedQuantileLoss      For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.  
     */
    MetricName: AutoMLMetricEnum;
  }
  export type AutoMLJobObjectiveType = "Maximize"|"Minimize"|string;
  export type AutoMLJobSecondaryStatus = "Starting"|"MaxCandidatesReached"|"Failed"|"Stopped"|"MaxAutoMLJobRuntimeReached"|"Stopping"|"CandidateDefinitionsGenerated"|"Completed"|"ExplainabilityError"|"DeployingModel"|"ModelDeploymentError"|"GeneratingModelInsightsReport"|"ModelInsightsError"|"AnalyzingData"|"FeatureEngineering"|"ModelTuning"|"GeneratingExplainabilityReport"|"TrainingModels"|"PreTraining"|string;
  export type AutoMLJobStatus = "Completed"|"InProgress"|"Failed"|"Stopped"|"Stopping"|string;
  export interface AutoMLJobStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the AutoML job.
     */
    Arn?: AutoMLJobArn;
  }
  export type AutoMLJobSummaries = AutoMLJobSummary[];
  export interface AutoMLJobSummary {
    /**
     * The name of the AutoML job you are requesting.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * The ARN of the AutoML job.
     */
    AutoMLJobArn: AutoMLJobArn;
    /**
     * The status of the AutoML job.
     */
    AutoMLJobStatus: AutoMLJobStatus;
    /**
     * The secondary status of the AutoML job.
     */
    AutoMLJobSecondaryStatus: AutoMLJobSecondaryStatus;
    /**
     * When the AutoML job was created.
     */
    CreationTime: Timestamp;
    /**
     * The end time of an AutoML job.
     */
    EndTime?: Timestamp;
    /**
     * When the AutoML job was last modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The failure reason of an AutoML job.
     */
    FailureReason?: AutoMLFailureReason;
    /**
     * The list of reasons for partial failures within an AutoML job.
     */
    PartialFailureReasons?: AutoMLPartialFailureReasons;
  }
  export type AutoMLMaxResults = number;
  export type AutoMLMaxResultsForTrials = number;
  export type AutoMLMetricEnum = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"BalancedAccuracy"|"R2"|"Recall"|"RecallMacro"|"Precision"|"PrecisionMacro"|"MAE"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"|string;
  export type AutoMLMetricExtendedEnum = "Accuracy"|"MSE"|"F1"|"F1macro"|"AUC"|"RMSE"|"MAE"|"R2"|"BalancedAccuracy"|"Precision"|"PrecisionMacro"|"Recall"|"RecallMacro"|"LogLoss"|"InferenceLatency"|"MAPE"|"MASE"|"WAPE"|"AverageWeightedQuantileLoss"|"Rouge1"|"Rouge2"|"RougeL"|"RougeLSum"|"Perplexity"|"ValidationLoss"|"TrainingLoss"|string;
  export type AutoMLMode = "AUTO"|"ENSEMBLING"|"HYPERPARAMETER_TUNING"|string;
  export type AutoMLNameContains = string;
  export interface AutoMLOutputDataConfig {
    /**
     * The Key Management Service encryption key ID.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The Amazon S3 output path. Must be 512 characters or less.
     */
    S3OutputPath: S3Uri;
  }
  export interface AutoMLPartialFailureReason {
    /**
     * The message containing the reason for a partial failure of an AutoML job.
     */
    PartialFailureMessage?: AutoMLFailureReason;
  }
  export type AutoMLPartialFailureReasons = AutoMLPartialFailureReason[];
  export interface AutoMLProblemTypeConfig {
    /**
     * Settings used to configure an AutoML job V2 for the image classification problem type.
     */
    ImageClassificationJobConfig?: ImageClassificationJobConfig;
    /**
     * Settings used to configure an AutoML job V2 for the text classification problem type.
     */
    TextClassificationJobConfig?: TextClassificationJobConfig;
    /**
     * Settings used to configure an AutoML job V2 for the time-series forecasting problem type.
     */
    TimeSeriesForecastingJobConfig?: TimeSeriesForecastingJobConfig;
    /**
     * Settings used to configure an AutoML job V2 for the tabular problem type (regression, classification).
     */
    TabularJobConfig?: TabularJobConfig;
    /**
     * Settings used to configure an AutoML job V2 for the text generation (LLMs fine-tuning) problem type.  The text generation models that support fine-tuning in Autopilot are currently accessible exclusively in regions supported by Canvas. Refer to the documentation of Canvas for the full list of its supported Regions. 
     */
    TextGenerationJobConfig?: TextGenerationJobConfig;
  }
  export type AutoMLProblemTypeConfigName = "ImageClassification"|"TextClassification"|"TimeSeriesForecasting"|"Tabular"|"TextGeneration"|string;
  export interface AutoMLProblemTypeResolvedAttributes {
    /**
     * The resolved attributes for the tabular problem type.
     */
    TabularResolvedAttributes?: TabularResolvedAttributes;
    /**
     * The resolved attributes for the text generation problem type.
     */
    TextGenerationResolvedAttributes?: TextGenerationResolvedAttributes;
  }
  export type AutoMLProcessingUnit = "CPU"|"GPU"|string;
  export interface AutoMLResolvedAttributes {
    AutoMLJobObjective?: AutoMLJobObjective;
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * Defines the resolved attributes specific to a problem type.
     */
    AutoMLProblemTypeResolvedAttributes?: AutoMLProblemTypeResolvedAttributes;
  }
  export interface AutoMLS3DataSource {
    /**
     * The data type.    If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training. The S3Prefix should have the following format:  s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER-OR-FILE    If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training. A ManifestFile should have the format shown below:  [ {"prefix": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/DOC-EXAMPLE-PREFIX/"},    "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-1",   "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-2",   ... "DOC-EXAMPLE-RELATIVE-PATH/DOC-EXAMPLE-FOLDER/DATA-N" ]    If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile is available for V2 API jobs only (for example, for jobs created by calling CreateAutoMLJobV2). Here is a minimal, single-record example of an AugmentedManifestFile:  {"source-ref": "s3://DOC-EXAMPLE-BUCKET/DOC-EXAMPLE-FOLDER/cats/cat.jpg",   "label-metadata": {"class-name": "cat" } For more information on AugmentedManifestFile, see Provide Dataset Metadata to Training Jobs with an Augmented Manifest File.  
     */
    S3DataType: AutoMLS3DataType;
    /**
     * The URL to the Amazon S3 data source. The Uri refers to the Amazon S3 prefix or ManifestFile depending on the data type.
     */
    S3Uri: S3Uri;
  }
  export type AutoMLS3DataType = "ManifestFile"|"S3Prefix"|"AugmentedManifestFile"|string;
  export interface AutoMLSecurityConfig {
    /**
     * The key used to encrypt stored data.
     */
    VolumeKmsKeyId?: KmsKeyId;
    /**
     * Whether to use traffic encryption between the container layers.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * The VPC configuration.
     */
    VpcConfig?: VpcConfig;
  }
  export type AutoMLSortBy = "Name"|"CreationTime"|"Status"|string;
  export type AutoMLSortOrder = "Ascending"|"Descending"|string;
  export type AutoMountHomeEFS = "Enabled"|"Disabled"|"DefaultAsDomain"|string;
  export interface AutoParameter {
    /**
     * The name of the hyperparameter to optimize using Autotune.
     */
    Name: ParameterKey;
    /**
     * An example value of the hyperparameter to optimize using Autotune.
     */
    ValueHint: ParameterValue;
  }
  export type AutoParameters = AutoParameter[];
  export interface AutoRollbackConfig {
    /**
     * List of CloudWatch alarms in your account that are configured to monitor metrics on an endpoint. If any alarms are tripped during a deployment, SageMaker rolls back the deployment.
     */
    Alarms?: AlarmList;
  }
  export interface Autotune {
    /**
     * Set Mode to Enabled if you want to use Autotune.
     */
    Mode: AutotuneMode;
  }
  export type AutotuneMode = "Enabled"|string;
  export type AwsManagedHumanLoopRequestSource = "AWS/Rekognition/DetectModerationLabels/Image/V3"|"AWS/Textract/AnalyzeDocument/Forms/V1"|string;
  export type BacktestResultsLocation = string;
  export type BaseModelName = string;
  export interface BatchDataCaptureConfig {
    /**
     * The Amazon S3 location being used to capture the data.
     */
    DestinationS3Uri: S3Uri;
    /**
     * The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the batch transform job. The KmsKeyId can be any of the following formats:    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias   
     */
    KmsKeyId?: KmsKeyId;
    /**
     * Flag that indicates whether to append inference id to the output.
     */
    GenerateInferenceId?: Boolean;
  }
  export interface BatchDescribeModelPackageError {
    /**
     * 
     */
    ErrorCode: String;
    /**
     * 
     */
    ErrorResponse: String;
  }
  export type BatchDescribeModelPackageErrorMap = {[key: string]: BatchDescribeModelPackageError};
  export interface BatchDescribeModelPackageInput {
    /**
     * The list of Amazon Resource Name (ARN) of the model package groups.
     */
    ModelPackageArnList: ModelPackageArnList;
  }
  export interface BatchDescribeModelPackageOutput {
    /**
     * The summaries for the model package versions
     */
    ModelPackageSummaries?: ModelPackageSummaries;
    /**
     * A map of the resource and BatchDescribeModelPackageError objects reporting the error associated with describing the model package.
     */
    BatchDescribeModelPackageErrorMap?: BatchDescribeModelPackageErrorMap;
  }
  export interface BatchDescribeModelPackageSummary {
    /**
     * The group name for the model package
     */
    ModelPackageGroupName: EntityName;
    /**
     * The version number of a versioned model.
     */
    ModelPackageVersion?: ModelPackageVersion;
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    ModelPackageArn: ModelPackageArn;
    /**
     * The description of the model package.
     */
    ModelPackageDescription?: EntityDescription;
    /**
     * The creation time of the mortgage package summary.
     */
    CreationTime: CreationTime;
    InferenceSpecification: InferenceSpecification;
    /**
     * The status of the mortgage package.
     */
    ModelPackageStatus: ModelPackageStatus;
    /**
     * The approval status of the model.
     */
    ModelApprovalStatus?: ModelApprovalStatus;
  }
  export type BatchStrategy = "MultiRecord"|"SingleRecord"|string;
  export interface BatchTransformInput {
    /**
     * The Amazon S3 location being used to capture the data.
     */
    DataCapturedDestinationS3Uri: DestinationS3Uri;
    /**
     * The dataset format for your batch transform job.
     */
    DatasetFormat: MonitoringDatasetFormat;
    /**
     * Path to the filesystem where the batch transform data is available to the container.
     */
    LocalPath: ProcessingLocalPath;
    /**
     * Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.
     */
    S3InputMode?: ProcessingS3InputMode;
    /**
     * Whether input data distributed in Amazon S3 is fully replicated or sharded by an S3 key. Defaults to FullyReplicated 
     */
    S3DataDistributionType?: ProcessingS3DataDistributionType;
    /**
     * The attributes of the input data that are the input features.
     */
    FeaturesAttribute?: String;
    /**
     * The attribute of the input data that represents the ground truth label.
     */
    InferenceAttribute?: String;
    /**
     * In a classification problem, the attribute that represents the class probability.
     */
    ProbabilityAttribute?: String;
    /**
     * The threshold for the class probability to be evaluated as a positive result.
     */
    ProbabilityThresholdAttribute?: ProbabilityThresholdAttribute;
    /**
     * If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
     */
    StartTimeOffset?: MonitoringTimeOffsetString;
    /**
     * If specified, monitoring jobs subtract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
     */
    EndTimeOffset?: MonitoringTimeOffsetString;
    /**
     * The attributes of the input data to exclude from the analysis.
     */
    ExcludeFeaturesAttribute?: ExcludeFeaturesAttribute;
  }
  export interface BestObjectiveNotImproving {
    /**
     * The number of training jobs that have failed to improve model performance by 1% or greater over prior training jobs as evaluated against an objective function.
     */
    MaxNumberOfTrainingJobsNotImproving?: MaxNumberOfTrainingJobsNotImproving;
  }
  export interface Bias {
    /**
     * The bias report for a model
     */
    Report?: MetricsSource;
    /**
     * The pre-training bias report for a model.
     */
    PreTrainingReport?: MetricsSource;
    /**
     * The post-training bias report for a model.
     */
    PostTrainingReport?: MetricsSource;
  }
  export type BillableTimeInSeconds = number;
  export type BlockedReason = string;
  export interface BlueGreenUpdatePolicy {
    /**
     * Defines the traffic routing strategy to shift traffic from the old fleet to the new fleet during an endpoint deployment.
     */
    TrafficRoutingConfiguration: TrafficRoutingConfig;
    /**
     * Additional waiting time in seconds after the completion of an endpoint deployment before terminating the old endpoint fleet. Default is 0.
     */
    TerminationWaitInSeconds?: TerminationWaitInSeconds;
    /**
     * Maximum execution timeout for the deployment. Note that the timeout value should be larger than the total waiting time specified in TerminationWaitInSeconds and WaitIntervalInSeconds.
     */
    MaximumExecutionTimeoutInSeconds?: MaximumExecutionTimeoutInSeconds;
  }
  export type Boolean = boolean;
  export type BooleanOperator = "And"|"Or"|string;
  export type Branch = string;
  export type BucketName = string;
  export interface CacheHitResult {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    SourcePipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface CallbackStepMetadata {
    /**
     * The pipeline generated token from the Amazon SQS queue.
     */
    CallbackToken?: CallbackToken;
    /**
     * The URL of the Amazon Simple Queue Service (Amazon SQS) queue used by the callback step.
     */
    SqsQueueUrl?: String256;
    /**
     * A list of the output parameters of the callback step.
     */
    OutputParameters?: OutputParameterList;
  }
  export type CallbackToken = string;
  export interface CandidateArtifactLocations {
    /**
     * The Amazon S3 prefix to the explainability artifacts generated for the AutoML candidate.
     */
    Explainability: ExplainabilityLocation;
    /**
     * The Amazon S3 prefix to the model insight artifacts generated for the AutoML candidate.
     */
    ModelInsights?: ModelInsightsLocation;
    /**
     * The Amazon S3 prefix to the accuracy metrics and the inference results observed over the testing window. Available only for the time-series forecasting problem type.
     */
    BacktestResults?: BacktestResultsLocation;
  }
  export type CandidateDefinitionNotebookLocation = string;
  export interface CandidateGenerationConfig {
    /**
     * Your Autopilot job trains a default set of algorithms on your dataset. For tabular and time-series data, you can customize the algorithm list by selecting a subset of algorithms for your problem type.  AlgorithmsConfig stores the customized selection of algorithms to train on your data.    For the tabular problem type TabularJobConfig, the list of available algorithms to choose from depends on the training mode set in  AutoMLJobConfig.Mode .    AlgorithmsConfig should not be set when the training mode AutoMLJobConfig.Mode is set to AUTO.   When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for the given training mode.   When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for the given training mode.   For the list of all algorithms per training mode, see  AlgorithmConfig. For more information on each algorithm, see the Algorithm support section in the Autopilot developer guide.    For the time-series forecasting problem type TimeSeriesForecastingJobConfig, choose your algorithms from the list provided in  AlgorithmConfig. For more information on each algorithm, see the Algorithms support for time-series forecasting section in the Autopilot developer guide.   When AlgorithmsConfig is provided, one AutoMLAlgorithms attribute must be set and one only. If the list of algorithms provided as values for AutoMLAlgorithms is empty, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.   When AlgorithmsConfig is not provided, CandidateGenerationConfig uses the full set of algorithms for time-series forecasting.    
     */
    AlgorithmsConfig?: AutoMLAlgorithmsConfig;
  }
  export type CandidateName = string;
  export interface CandidateProperties {
    /**
     * The Amazon S3 prefix to the artifacts generated for an AutoML candidate.
     */
    CandidateArtifactLocations?: CandidateArtifactLocations;
    /**
     * Information about the candidate metrics for an AutoML job.
     */
    CandidateMetrics?: MetricDataList;
  }
  export type CandidateSortBy = "CreationTime"|"Status"|"FinalObjectiveMetricValue"|string;
  export type CandidateStatus = "Completed"|"InProgress"|"Failed"|"Stopped"|"Stopping"|string;
  export type CandidateStepArn = string;
  export type CandidateStepName = string;
  export type CandidateStepType = "AWS::SageMaker::TrainingJob"|"AWS::SageMaker::TransformJob"|"AWS::SageMaker::ProcessingJob"|string;
  export type CandidateSteps = AutoMLCandidateStep[];
  export interface CanvasAppSettings {
    /**
     * Time series forecast settings for the SageMaker Canvas application.
     */
    TimeSeriesForecastingSettings?: TimeSeriesForecastingSettings;
    /**
     * The model registry settings for the SageMaker Canvas application.
     */
    ModelRegisterSettings?: ModelRegisterSettings;
    /**
     * The workspace settings for the SageMaker Canvas application.
     */
    WorkspaceSettings?: WorkspaceSettings;
    /**
     * The settings for connecting to an external data source with OAuth.
     */
    IdentityProviderOAuthSettings?: IdentityProviderOAuthSettings;
    /**
     * The model deployment settings for the SageMaker Canvas application.
     */
    DirectDeploySettings?: DirectDeploySettings;
    /**
     * The settings for document querying.
     */
    KendraSettings?: KendraSettings;
    /**
     * The generative AI settings for the SageMaker Canvas application.
     */
    GenerativeAiSettings?: GenerativeAiSettings;
    /**
     * The settings for running Amazon EMR Serverless data processing jobs in SageMaker Canvas.
     */
    EmrServerlessSettings?: EmrServerlessSettings;
  }
  export interface CapacitySize {
    /**
     * Specifies the endpoint capacity type.    INSTANCE_COUNT: The endpoint activates based on the number of instances.    CAPACITY_PERCENT: The endpoint activates based on the specified percentage of capacity.  
     */
    Type: CapacitySizeType;
    /**
     * Defines the capacity size, either as a number of instances or a capacity percentage.
     */
    Value: CapacitySizeValue;
  }
  export type CapacitySizeType = "INSTANCE_COUNT"|"CAPACITY_PERCENT"|string;
  export type CapacitySizeValue = number;
  export type CapacityUnit = number;
  export interface CaptureContentTypeHeader {
    /**
     * The list of all content type headers that Amazon SageMaker will treat as CSV and capture accordingly.
     */
    CsvContentTypes?: CsvContentTypes;
    /**
     * The list of all content type headers that SageMaker will treat as JSON and capture accordingly.
     */
    JsonContentTypes?: JsonContentTypes;
  }
  export type CaptureMode = "Input"|"Output"|"InputAndOutput"|string;
  export interface CaptureOption {
    /**
     * Specify the boundary of data to capture.
     */
    CaptureMode: CaptureMode;
  }
  export type CaptureOptionList = CaptureOption[];
  export type CaptureStatus = "Started"|"Stopped"|string;
  export type Catalog = string;
  export interface CategoricalParameter {
    /**
     * The Name of the environment variable.
     */
    Name: String64;
    /**
     * The list of values you can pass.
     */
    Value: CategoricalParameterRangeValues;
  }
  export interface CategoricalParameterRange {
    /**
     * The name of the categorical hyperparameter to tune.
     */
    Name: ParameterKey;
    /**
     * A list of the categories for the hyperparameter.
     */
    Values: ParameterValues;
  }
  export interface CategoricalParameterRangeSpecification {
    /**
     * The allowed categories for the hyperparameter.
     */
    Values: ParameterValues;
  }
  export type CategoricalParameterRangeValues = String128[];
  export type CategoricalParameterRanges = CategoricalParameterRange[];
  export type CategoricalParameters = CategoricalParameter[];
  export type Cents = number;
  export type CertifyForMarketplace = boolean;
  export interface Channel {
    /**
     * The name of the channel. 
     */
    ChannelName: ChannelName;
    /**
     * The location of the channel data.
     */
    DataSource: DataSource;
    /**
     * The MIME type of the data.
     */
    ContentType?: ContentType;
    /**
     * If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.
     */
    CompressionType?: CompressionType;
    /**
     *  Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format. In this case, SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.  In File mode, leave this field unset or set it to None.
     */
    RecordWrapperType?: RecordWrapper;
    /**
     * (Optional) The input mode to use for the data channel in a training job. If you don't set a value for InputMode, SageMaker uses the value set for TrainingInputMode. Use this parameter to override the TrainingInputMode setting in a AlgorithmSpecification request when you have a channel that needs a different input mode from the training job's general setting. To download the data from Amazon Simple Storage Service (Amazon S3) to the provisioned ML storage volume, and mount the directory to a Docker volume, use File input mode. To stream data directly from Amazon S3 to the container, choose Pipe input mode. To use a model for incremental training, choose File input model.
     */
    InputMode?: TrainingInputMode;
    /**
     * A configuration for a shuffle option for input data in a channel. If you use S3Prefix for S3DataType, this shuffles the results of the S3 key prefix matches. If you use ManifestFile, the order of the S3 object references in the ManifestFile is shuffled. If you use AugmentedManifestFile, the order of the JSON lines in the AugmentedManifestFile is shuffled. The shuffling order is determined using the Seed value. For Pipe input mode, shuffling is done at the start of every epoch. With large datasets this ensures that the order of the training data is different for each epoch, it helps reduce bias and possible overfitting. In a multi-node training job when ShuffleConfig is combined with S3DataDistributionType of ShardedByS3Key, the data is shuffled across nodes so that the content sent to a particular node on the first epoch might be sent to a different node on the second epoch.
     */
    ShuffleConfig?: ShuffleConfig;
  }
  export type ChannelName = string;
  export interface ChannelSpecification {
    /**
     * The name of the channel.
     */
    Name: ChannelName;
    /**
     * A brief description of the channel.
     */
    Description?: EntityDescription;
    /**
     * Indicates whether the channel is required by the algorithm.
     */
    IsRequired?: Boolean;
    /**
     * The supported MIME types for the data.
     */
    SupportedContentTypes: ContentTypes;
    /**
     * The allowed compression types, if data compression is used.
     */
    SupportedCompressionTypes?: CompressionTypes;
    /**
     * The allowed input mode, either FILE or PIPE. In FILE mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In PIPE mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
     */
    SupportedInputModes: InputModes;
  }
  export type ChannelSpecifications = ChannelSpecification[];
  export interface CheckpointConfig {
    /**
     * Identifies the S3 path where you want SageMaker to store checkpoints. For example, s3://bucket-name/key-name-prefix.
     */
    S3Uri: S3Uri;
    /**
     * (Optional) The local directory where checkpoints are written. The default directory is /opt/ml/checkpoints/. 
     */
    LocalPath?: DirectoryPath;
  }
  export type Cidr = string;
  export type Cidrs = Cidr[];
  export interface ClarifyCheckStepMetadata {
    /**
     * The type of the Clarify Check step
     */
    CheckType?: String256;
    /**
     * The Amazon S3 URI of baseline constraints file to be used for the drift check.
     */
    BaselineUsedForDriftCheckConstraints?: String1024;
    /**
     * The Amazon S3 URI of the newly calculated baseline constraints file.
     */
    CalculatedBaselineConstraints?: String1024;
    /**
     * The model package group name.
     */
    ModelPackageGroupName?: String256;
    /**
     * The Amazon S3 URI of the violation report if violations are detected.
     */
    ViolationReport?: String1024;
    /**
     * The Amazon Resource Name (ARN) of the check processing job that was run by this step's execution.
     */
    CheckJobArn?: String256;
    /**
     * This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
     */
    SkipCheck?: Boolean;
    /**
     * This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints property. 
     */
    RegisterNewBaseline?: Boolean;
  }
  export type ClarifyContentTemplate = string;
  export type ClarifyEnableExplanations = string;
  export interface ClarifyExplainerConfig {
    /**
     * A JMESPath boolean expression used to filter which records to explain. Explanations are activated by default. See  EnableExplanations for additional information.
     */
    EnableExplanations?: ClarifyEnableExplanations;
    /**
     * The inference configuration parameter for the model container.
     */
    InferenceConfig?: ClarifyInferenceConfig;
    /**
     * The configuration for SHAP analysis.
     */
    ShapConfig: ClarifyShapConfig;
  }
  export type ClarifyFeatureHeaders = ClarifyHeader[];
  export type ClarifyFeatureType = "numerical"|"categorical"|"text"|string;
  export type ClarifyFeatureTypes = ClarifyFeatureType[];
  export type ClarifyFeaturesAttribute = string;
  export type ClarifyHeader = string;
  export interface ClarifyInferenceConfig {
    /**
     * Provides the JMESPath expression to extract the features from a model container input in JSON Lines format. For example, if FeaturesAttribute is the JMESPath expression 'myfeatures', it extracts a list of features [1,2,3] from request data '{"myfeatures":[1,2,3]}'.
     */
    FeaturesAttribute?: ClarifyFeaturesAttribute;
    /**
     * A template string used to format a JSON record into an acceptable model container input. For example, a ContentTemplate string '{"myfeatures":$features}' will format a list of features [1,2,3] into the record string '{"myfeatures":[1,2,3]}'. Required only when the model container input is in JSON Lines format.
     */
    ContentTemplate?: ClarifyContentTemplate;
    /**
     * The maximum number of records in a request that the model container can process when querying the model container for the predictions of a synthetic dataset. A record is a unit of input data that inference can be made on, for example, a single line in CSV data. If MaxRecordCount is 1, the model container expects one record per request. A value of 2 or greater means that the model expects batch requests, which can reduce overhead and speed up the inferencing process. If this parameter is not provided, the explainer will tune the record count per request according to the model container's capacity at runtime.
     */
    MaxRecordCount?: ClarifyMaxRecordCount;
    /**
     * The maximum payload size (MB) allowed of a request from the explainer to the model container. Defaults to 6 MB.
     */
    MaxPayloadInMB?: ClarifyMaxPayloadInMB;
    /**
     * A zero-based index used to extract a probability value (score) or list from model container output in CSV format. If this value is not provided, the entire model container output will be treated as a probability value (score) or list.  Example for a single class model: If the model container output consists of a string-formatted prediction label followed by its probability: '1,0.6', set ProbabilityIndex to 1 to select the probability value 0.6.  Example for a multiclass model: If the model container output consists of a string-formatted prediction label followed by its probability: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set ProbabilityIndex to 1 to select the probability values [0.1,0.6,0.3].
     */
    ProbabilityIndex?: ClarifyProbabilityIndex;
    /**
     * A zero-based index used to extract a label header or list of label headers from model container output in CSV format.  Example for a multiclass model: If the model container output consists of label headers followed by probabilities: '"[\'cat\',\'dog\',\'fish\']","[0.1,0.6,0.3]"', set LabelIndex to 0 to select the label headers ['cat','dog','fish'].
     */
    LabelIndex?: ClarifyLabelIndex;
    /**
     * A JMESPath expression used to extract the probability (or score) from the model container output if the model container is in JSON Lines format.  Example: If the model container output of a single request is '{"predicted_label":1,"probability":0.6}', then set ProbabilityAttribute to 'probability'.
     */
    ProbabilityAttribute?: ClarifyProbabilityAttribute;
    /**
     * A JMESPath expression used to locate the list of label headers in the model container output.  Example: If the model container output of a batch request is '{"labels":["cat","dog","fish"],"probability":[0.6,0.3,0.1]}', then set LabelAttribute to 'labels' to extract the list of label headers ["cat","dog","fish"] 
     */
    LabelAttribute?: ClarifyLabelAttribute;
    /**
     * For multiclass classification problems, the label headers are the names of the classes. Otherwise, the label header is the name of the predicted label. These are used to help readability for the output of the InvokeEndpoint API. See the response section under Invoke the endpoint in the Developer Guide for more information. If there are no label headers in the model container output, provide them manually using this parameter.
     */
    LabelHeaders?: ClarifyLabelHeaders;
    /**
     * The names of the features. If provided, these are included in the endpoint response payload to help readability of the InvokeEndpoint output. See the Response section under Invoke the endpoint in the Developer Guide for more information.
     */
    FeatureHeaders?: ClarifyFeatureHeaders;
    /**
     * A list of data types of the features (optional). Applicable only to NLP explainability. If provided, FeatureTypes must have at least one 'text' string (for example, ['text']). If FeatureTypes is not provided, the explainer infers the feature types based on the baseline data. The feature types are included in the endpoint response payload. For additional information see the response section under Invoke the endpoint in the Developer Guide for more information.
     */
    FeatureTypes?: ClarifyFeatureTypes;
  }
  export type ClarifyLabelAttribute = string;
  export type ClarifyLabelHeaders = ClarifyHeader[];
  export type ClarifyLabelIndex = number;
  export type ClarifyMaxPayloadInMB = number;
  export type ClarifyMaxRecordCount = number;
  export type ClarifyMimeType = string;
  export type ClarifyProbabilityAttribute = string;
  export type ClarifyProbabilityIndex = number;
  export type ClarifyShapBaseline = string;
  export interface ClarifyShapBaselineConfig {
    /**
     * The MIME type of the baseline data. Choose from 'text/csv' or 'application/jsonlines'. Defaults to 'text/csv'.
     */
    MimeType?: ClarifyMimeType;
    /**
     * The inline SHAP baseline data in string format. ShapBaseline can have one or multiple records to be used as the baseline dataset. The format of the SHAP baseline file should be the same format as the training dataset. For example, if the training dataset is in CSV format and each record contains four features, and all features are numerical, then the format of the baseline data should also share these characteristics. For natural language processing (NLP) of text columns, the baseline value should be the value used to replace the unit of text specified by the Granularity of the TextConfig parameter. The size limit for ShapBasline is 4 KB. Use the ShapBaselineUri parameter if you want to provide more than 4 KB of baseline data.
     */
    ShapBaseline?: ClarifyShapBaseline;
    /**
     * The uniform resource identifier (URI) of the S3 bucket where the SHAP baseline file is stored. The format of the SHAP baseline file should be the same format as the format of the training dataset. For example, if the training dataset is in CSV format, and each record in the training dataset has four features, and all features are numerical, then the baseline file should also have this same format. Each record should contain only the features. If you are using a virtual private cloud (VPC), the ShapBaselineUri should be accessible to the VPC. For more information about setting up endpoints with Amazon Virtual Private Cloud, see Give SageMaker access to Resources in your Amazon Virtual Private Cloud.
     */
    ShapBaselineUri?: Url;
  }
  export interface ClarifyShapConfig {
    /**
     * The configuration for the SHAP baseline of the Kernal SHAP algorithm.
     */
    ShapBaselineConfig: ClarifyShapBaselineConfig;
    /**
     * The number of samples to be used for analysis by the Kernal SHAP algorithm.   The number of samples determines the size of the synthetic dataset, which has an impact on latency of explainability requests. For more information, see the Synthetic data of Configure and create an endpoint. 
     */
    NumberOfSamples?: ClarifyShapNumberOfSamples;
    /**
     * A Boolean toggle to indicate if you want to use the logit function (true) or log-odds units (false) for model predictions. Defaults to false.
     */
    UseLogit?: ClarifyShapUseLogit;
    /**
     * The starting value used to initialize the random number generator in the explainer. Provide a value for this parameter to obtain a deterministic SHAP result.
     */
    Seed?: ClarifyShapSeed;
    /**
     * A parameter that indicates if text features are treated as text and explanations are provided for individual units of text. Required for natural language processing (NLP) explainability only.
     */
    TextConfig?: ClarifyTextConfig;
  }
  export type ClarifyShapNumberOfSamples = number;
  export type ClarifyShapSeed = number;
  export type ClarifyShapUseLogit = boolean;
  export interface ClarifyTextConfig {
    /**
     * Specifies the language of the text features in ISO 639-1 or ISO 639-3 code of a supported language.   For a mix of multiple languages, use code 'xx'. 
     */
    Language: ClarifyTextLanguage;
    /**
     * The unit of granularity for the analysis of text features. For example, if the unit is 'token', then each token (like a word in English) of the text is treated as a feature. SHAP values are computed for each unit/feature.
     */
    Granularity: ClarifyTextGranularity;
  }
  export type ClarifyTextGranularity = "token"|"sentence"|"paragraph"|string;
  export type ClarifyTextLanguage = "af"|"sq"|"ar"|"hy"|"eu"|"bn"|"bg"|"ca"|"zh"|"hr"|"cs"|"da"|"nl"|"en"|"et"|"fi"|"fr"|"de"|"el"|"gu"|"he"|"hi"|"hu"|"is"|"id"|"ga"|"it"|"kn"|"ky"|"lv"|"lt"|"lb"|"mk"|"ml"|"mr"|"ne"|"nb"|"fa"|"pl"|"pt"|"ro"|"ru"|"sa"|"sr"|"tn"|"si"|"sk"|"sl"|"es"|"sv"|"tl"|"ta"|"tt"|"te"|"tr"|"uk"|"ur"|"yo"|"lij"|"xx"|string;
  export type ClientId = string;
  export type ClientSecret = string;
  export type ClientToken = string;
  export type ClusterArn = string;
  export type ClusterAvailabilityZone = string;
  export type ClusterAvailabilityZoneId = string;
  export interface ClusterEbsVolumeConfig {
    /**
     * The size in gigabytes (GB) of the additional EBS volume to be attached to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.
     */
    VolumeSizeInGB: ClusterEbsVolumeSizeInGB;
  }
  export type ClusterEbsVolumeSizeInGB = number;
  export type ClusterInstanceCount = number;
  export interface ClusterInstanceGroupDetails {
    /**
     * The number of instances that are currently in the instance group of a SageMaker HyperPod cluster.
     */
    CurrentCount?: ClusterNonNegativeInstanceCount;
    /**
     * The number of instances you specified to add to the instance group of a SageMaker HyperPod cluster.
     */
    TargetCount?: ClusterInstanceCount;
    /**
     * The name of the instance group of a SageMaker HyperPod cluster.
     */
    InstanceGroupName?: ClusterInstanceGroupName;
    /**
     * The instance type of the instance group of a SageMaker HyperPod cluster.
     */
    InstanceType?: ClusterInstanceType;
    /**
     * Details of LifeCycle configuration for the instance group.
     */
    LifeCycleConfig?: ClusterLifeCycleConfig;
    /**
     * The execution role for the instance group to assume.
     */
    ExecutionRole?: IAMRoleArn;
    /**
     * The number you specified to TreadsPerCore in CreateCluster for enabling or disabling multithreading. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
     */
    ThreadsPerCore?: ClusterThreadsPerCore;
    /**
     * The additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
     */
    InstanceStorageConfigs?: ClusterInstanceStorageConfigs;
  }
  export type ClusterInstanceGroupDetailsList = ClusterInstanceGroupDetails[];
  export type ClusterInstanceGroupName = string;
  export interface ClusterInstanceGroupSpecification {
    /**
     * Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.
     */
    InstanceCount: ClusterInstanceCount;
    /**
     * Specifies the name of the instance group.
     */
    InstanceGroupName: ClusterInstanceGroupName;
    /**
     * Specifies the instance type of the instance group.
     */
    InstanceType: ClusterInstanceType;
    /**
     * Specifies the LifeCycle configuration for the instance group.
     */
    LifeCycleConfig: ClusterLifeCycleConfig;
    /**
     * Specifies an IAM execution role to be assumed by the instance group.
     */
    ExecutionRole: IAMRoleArn;
    /**
     * Specifies the value for Threads per core. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn't support multithreading, specify 1. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.
     */
    ThreadsPerCore?: ClusterThreadsPerCore;
    /**
     * Specifies the additional storage configurations for the instances in the SageMaker HyperPod cluster instance group.
     */
    InstanceStorageConfigs?: ClusterInstanceStorageConfigs;
  }
  export type ClusterInstanceGroupSpecifications = ClusterInstanceGroupSpecification[];
  export interface ClusterInstancePlacement {
    /**
     * The Availability Zone where the node in the SageMaker HyperPod cluster is launched.
     */
    AvailabilityZone?: ClusterAvailabilityZone;
    /**
     * The unique identifier (ID) of the Availability Zone where the node in the SageMaker HyperPod cluster is launched.
     */
    AvailabilityZoneId?: ClusterAvailabilityZoneId;
  }
  export type ClusterInstanceStatus = "Running"|"Failure"|"Pending"|"ShuttingDown"|"SystemUpdating"|string;
  export interface ClusterInstanceStatusDetails {
    /**
     * The status of an instance in a SageMaker HyperPod cluster.
     */
    Status: ClusterInstanceStatus;
    /**
     * The message from an instance in a SageMaker HyperPod cluster.
     */
    Message?: String;
  }
  export interface ClusterInstanceStorageConfig {
    /**
     * Defines the configuration for attaching additional Amazon Elastic Block Store (EBS) volumes to the instances in the SageMaker HyperPod cluster instance group. The additional EBS volume is attached to each instance within the SageMaker HyperPod cluster instance group and mounted to /opt/sagemaker.
     */
    EbsVolumeConfig?: ClusterEbsVolumeConfig;
  }
  export type ClusterInstanceStorageConfigs = ClusterInstanceStorageConfig[];
  export type ClusterInstanceType = "ml.p4d.24xlarge"|"ml.p4de.24xlarge"|"ml.p5.48xlarge"|"ml.trn1.32xlarge"|"ml.trn1n.32xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.12xlarge"|"ml.g5.16xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.c5.large"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.12xlarge"|"ml.c5.18xlarge"|"ml.c5.24xlarge"|"ml.c5n.large"|"ml.c5n.2xlarge"|"ml.c5n.4xlarge"|"ml.c5n.9xlarge"|"ml.c5n.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.8xlarge"|"ml.m5.12xlarge"|"ml.m5.16xlarge"|"ml.m5.24xlarge"|"ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|string;
  export interface ClusterLifeCycleConfig {
    /**
     * An Amazon S3 bucket path where your lifecycle scripts are stored.  Make sure that the S3 bucket path starts with s3://sagemaker-. The IAM role for SageMaker HyperPod has the managed  AmazonSageMakerClusterInstanceRolePolicy  attached, which allows access to S3 buckets with the specific prefix sagemaker-. 
     */
    SourceS3Uri: S3Uri;
    /**
     * The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This entrypoint script runs during cluster creation.
     */
    OnCreate: ClusterLifeCycleConfigFileName;
  }
  export type ClusterLifeCycleConfigFileName = string;
  export type ClusterName = string;
  export type ClusterNameOrArn = string;
  export interface ClusterNodeDetails {
    /**
     * The instance group name in which the instance is.
     */
    InstanceGroupName?: ClusterInstanceGroupName;
    /**
     * The ID of the instance.
     */
    InstanceId?: String;
    /**
     * The status of the instance.
     */
    InstanceStatus?: ClusterInstanceStatusDetails;
    /**
     * The type of the instance.
     */
    InstanceType?: ClusterInstanceType;
    /**
     * The time when the instance is launched.
     */
    LaunchTime?: Timestamp;
    /**
     * The LifeCycle configuration applied to the instance.
     */
    LifeCycleConfig?: ClusterLifeCycleConfig;
    /**
     * The number of threads per CPU core you specified under CreateCluster.
     */
    ThreadsPerCore?: ClusterThreadsPerCore;
    /**
     * The configurations of additional storage specified to the instance group where the instance (node) is launched.
     */
    InstanceStorageConfigs?: ClusterInstanceStorageConfigs;
    /**
     * The private primary IP address of the SageMaker HyperPod cluster node.
     */
    PrivatePrimaryIp?: ClusterPrivatePrimaryIp;
    /**
     * The private DNS hostname of the SageMaker HyperPod cluster node.
     */
    PrivateDnsHostname?: ClusterPrivateDnsHostname;
    /**
     * The placement details of the SageMaker HyperPod cluster node.
     */
    Placement?: ClusterInstancePlacement;
  }
  export type ClusterNodeId = string;
  export type ClusterNodeSummaries = ClusterNodeSummary[];
  export interface ClusterNodeSummary {
    /**
     * The name of the instance group in which the instance is.
     */
    InstanceGroupName: ClusterInstanceGroupName;
    /**
     * The ID of the instance.
     */
    InstanceId: String;
    /**
     * The type of the instance.
     */
    InstanceType: ClusterInstanceType;
    /**
     * The time when the instance is launched.
     */
    LaunchTime: Timestamp;
    /**
     * The status of the instance.
     */
    InstanceStatus: ClusterInstanceStatusDetails;
  }
  export type ClusterNonNegativeInstanceCount = number;
  export type ClusterPrivateDnsHostname = string;
  export type ClusterPrivatePrimaryIp = string;
  export type ClusterSortBy = "CREATION_TIME"|"NAME"|string;
  export type ClusterStatus = "Creating"|"Deleting"|"Failed"|"InService"|"RollingBack"|"SystemUpdating"|"Updating"|string;
  export type ClusterSummaries = ClusterSummary[];
  export interface ClusterSummary {
    /**
     * The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
     */
    ClusterArn: ClusterArn;
    /**
     * The name of the SageMaker HyperPod cluster.
     */
    ClusterName: ClusterName;
    /**
     * The time when the SageMaker HyperPod cluster is created.
     */
    CreationTime: Timestamp;
    /**
     * The status of the SageMaker HyperPod cluster.
     */
    ClusterStatus: ClusterStatus;
  }
  export type ClusterThreadsPerCore = number;
  export interface CodeEditorAppImageConfig {
    FileSystemConfig?: FileSystemConfig;
    ContainerConfig?: ContainerConfig;
  }
  export interface CodeEditorAppSettings {
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A list of custom SageMaker images that are configured to run as a Code Editor app.
     */
    CustomImages?: CustomImages;
    /**
     * The Amazon Resource Name (ARN) of the Code Editor application lifecycle configuration.
     */
    LifecycleConfigArns?: LifecycleConfigArns;
    /**
     * Settings that are used to configure and manage the lifecycle of CodeEditor applications. 
     */
    AppLifecycleManagement?: AppLifecycleManagement;
  }
  export type CodeRepositories = CodeRepository[];
  export interface CodeRepository {
    /**
     * The URL of the Git repository.
     */
    RepositoryUrl: RepositoryUrl;
  }
  export type CodeRepositoryArn = string;
  export type CodeRepositoryContains = string;
  export type CodeRepositoryNameContains = string;
  export type CodeRepositoryNameOrUrl = string;
  export type CodeRepositorySortBy = "Name"|"CreationTime"|"LastModifiedTime"|string;
  export type CodeRepositorySortOrder = "Ascending"|"Descending"|string;
  export interface CodeRepositorySummary {
    /**
     * The name of the Git repository.
     */
    CodeRepositoryName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the Git repository.
     */
    CodeRepositoryArn: CodeRepositoryArn;
    /**
     * The date and time that the Git repository was created.
     */
    CreationTime: CreationTime;
    /**
     * The date and time that the Git repository was last modified.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * Configuration details for the Git repository, including the URL where it is located and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
     */
    GitConfig?: GitConfig;
  }
  export type CodeRepositorySummaryList = CodeRepositorySummary[];
  export interface CognitoConfig {
    /**
     * A  user pool is a user directory in Amazon Cognito. With a user pool, your users can sign in to your web or mobile app through Amazon Cognito. Your users can also sign in through social identity providers like Google, Facebook, Amazon, or Apple, and through SAML identity providers.
     */
    UserPool: CognitoUserPool;
    /**
     * The client ID for your Amazon Cognito user pool.
     */
    ClientId: ClientId;
  }
  export interface CognitoMemberDefinition {
    /**
     * An identifier for a user pool. The user pool must be in the same region as the service that you are calling.
     */
    UserPool: CognitoUserPool;
    /**
     * An identifier for a user group.
     */
    UserGroup: CognitoUserGroup;
    /**
     * An identifier for an application client. You must create the app client ID using Amazon Cognito.
     */
    ClientId: ClientId;
  }
  export type CognitoUserGroup = string;
  export type CognitoUserPool = string;
  export interface CollectionConfig {
    /**
     * Configuration for your vector collection type.    Dimension: The number of elements in your vector.  
     */
    VectorConfig?: VectorConfig;
  }
  export interface CollectionConfiguration {
    /**
     * The name of the tensor collection. The name must be unique relative to other rule configuration names.
     */
    CollectionName?: CollectionName;
    /**
     * Parameter values for the tensor collection. The allowed parameters are "name", "include_regex", "reduction_config", "save_config", "tensor_names", and "save_histogram".
     */
    CollectionParameters?: CollectionParameters;
  }
  export type CollectionConfigurations = CollectionConfiguration[];
  export type CollectionName = string;
  export type CollectionParameters = {[key: string]: ConfigValue};
  export type CollectionType = "List"|"Set"|"Vector"|string;
  export type CompilationJobArn = string;
  export type CompilationJobStatus = "INPROGRESS"|"COMPLETED"|"FAILED"|"STARTING"|"STOPPING"|"STOPPED"|string;
  export type CompilationJobSummaries = CompilationJobSummary[];
  export interface CompilationJobSummary {
    /**
     * The name of the model compilation job that you want a summary for.
     */
    CompilationJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model compilation job.
     */
    CompilationJobArn: CompilationJobArn;
    /**
     * The time when the model compilation job was created.
     */
    CreationTime: CreationTime;
    /**
     * The time when the model compilation job started.
     */
    CompilationStartTime?: Timestamp;
    /**
     * The time when the model compilation job completed.
     */
    CompilationEndTime?: Timestamp;
    /**
     * The type of device that the model will run on after the compilation job has completed.
     */
    CompilationTargetDevice?: TargetDevice;
    /**
     * The type of OS that the model will run on after the compilation job has completed.
     */
    CompilationTargetPlatformOs?: TargetPlatformOs;
    /**
     * The type of architecture that the model will run on after the compilation job has completed.
     */
    CompilationTargetPlatformArch?: TargetPlatformArch;
    /**
     * The type of accelerator that the model will run on after the compilation job has completed.
     */
    CompilationTargetPlatformAccelerator?: TargetPlatformAccelerator;
    /**
     * The time when the model compilation job was last modified.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The status of the model compilation job.
     */
    CompilationJobStatus: CompilationJobStatus;
  }
  export type CompilerOptions = string;
  export type CompleteOnConvergence = "Disabled"|"Enabled"|string;
  export type CompressionType = "None"|"Gzip"|string;
  export type CompressionTypes = CompressionType[];
  export type ConditionOutcome = "True"|"False"|string;
  export interface ConditionStepMetadata {
    /**
     * The outcome of the Condition step evaluation.
     */
    Outcome?: ConditionOutcome;
  }
  export type ConfigKey = string;
  export type ConfigValue = string;
  export type ContainerArgument = string;
  export type ContainerArguments = ContainerArgument[];
  export interface ContainerConfig {
    /**
     * The arguments for the container when you're running the application.
     */
    ContainerArguments?: CustomImageContainerArguments;
    /**
     * The entrypoint used to run the application in the container.
     */
    ContainerEntrypoint?: CustomImageContainerEntrypoint;
    /**
     * The environment variables to set in the container
     */
    ContainerEnvironmentVariables?: CustomImageContainerEnvironmentVariables;
  }
  export interface ContainerDefinition {
    /**
     * This parameter is ignored for models that contain only a PrimaryContainer. When a ContainerDefinition is part of an inference pipeline, the value of the parameter uniquely identifies the container for the purposes of logging and metrics. For information, see Use Logs and Metrics to Monitor an Inference Pipeline. If you don't specify a value for this parameter for a ContainerDefinition that is part of an inference pipeline, a unique name is automatically assigned based on the position of the ContainerDefinition in the pipeline. If you specify a value for the ContainerHostName for any ContainerDefinition that is part of an inference pipeline, you must specify a value for the ContainerHostName parameter of every ContainerDefinition in that pipeline.
     */
    ContainerHostname?: ContainerHostname;
    /**
     * The path where inference code is stored. This can be either in Amazon EC2 Container Registry or in a Docker registry that is accessible from the same VPC that you configure for your endpoint. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.   The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. 
     */
    Image?: ContainerImage;
    /**
     * Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers.   The model artifacts in an Amazon S3 bucket and the Docker image for inference container in Amazon EC2 Container Registry must be in the same region as the model or endpoint you are creating. 
     */
    ImageConfig?: ImageConfig;
    /**
     * Whether the container hosts a single model or multiple models.
     */
    Mode?: ContainerMode;
    /**
     * The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.   The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.  If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.  If you use a built-in algorithm to create a model, SageMaker requires that you provide a S3 path to the model artifacts in ModelDataUrl. 
     */
    ModelDataUrl?: Url;
    /**
     * Specifies the location of ML model data to deploy.  Currently you cannot use ModelDataSource in conjunction with SageMaker batch transform, SageMaker serverless endpoints, SageMaker multi-model endpoints, and SageMaker Marketplace. 
     */
    ModelDataSource?: ModelDataSource;
    /**
     * Data sources that are available to your model in addition to the one that you specify for ModelDataSource when you use the CreateModel action.
     */
    AdditionalModelDataSources?: AdditionalModelDataSources;
    /**
     * The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. The maximum length of each key and value in the Environment map is 1024 bytes. The maximum length of all keys and values in the map, combined, is 32 KB. If you pass multiple containers to a CreateModel request, then the maximum length of all of their maps, combined, is also 32 KB.
     */
    Environment?: EnvironmentMap;
    /**
     * The name or Amazon Resource Name (ARN) of the model package to use to create the model.
     */
    ModelPackageName?: VersionedArnOrName;
    /**
     * The inference specification name in the model package version.
     */
    InferenceSpecificationName?: InferenceSpecificationName;
    /**
     * Specifies additional configuration for multi-model endpoints.
     */
    MultiModelConfig?: MultiModelConfig;
  }
  export type ContainerDefinitionList = ContainerDefinition[];
  export type ContainerEntrypoint = ContainerEntrypointString[];
  export type ContainerEntrypointString = string;
  export type ContainerHostname = string;
  export type ContainerImage = string;
  export type ContainerMode = "SingleModel"|"MultiModel"|string;
  export type ContentClassifier = "FreeOfPersonallyIdentifiableInformation"|"FreeOfAdultContent"|string;
  export type ContentClassifiers = ContentClassifier[];
  export type ContentColumn = string;
  export type ContentDigest = string;
  export type ContentType = string;
  export type ContentTypes = ContentType[];
  export type ContextArn = string;
  export type ContextName = string;
  export type ContextNameOrArn = string;
  export interface ContextSource {
    /**
     * The URI of the source.
     */
    SourceUri: SourceUri;
    /**
     * The type of the source.
     */
    SourceType?: String256;
    /**
     * The ID of the source.
     */
    SourceId?: String256;
  }
  export type ContextSummaries = ContextSummary[];
  export interface ContextSummary {
    /**
     * The Amazon Resource Name (ARN) of the context.
     */
    ContextArn?: ContextArn;
    /**
     * The name of the context.
     */
    ContextName?: ContextName;
    /**
     * The source of the context.
     */
    Source?: ContextSource;
    /**
     * The type of the context.
     */
    ContextType?: String256;
    /**
     * When the context was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the context was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export interface ContinuousParameterRange {
    /**
     * The name of the continuous hyperparameter to tune.
     */
    Name: ParameterKey;
    /**
     * The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.
     */
    MinValue: ParameterValue;
    /**
     * The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.
     */
    MaxValue: ParameterValue;
    /**
     * The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:  Auto  SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.  Linear  Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.  Logarithmic  Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.  ReverseLogarithmic  Hyperparameter tuning searches the values in the hyperparameter range by using a reverse logarithmic scale. Reverse logarithmic scaling works only for ranges that are entirely within the range 0&lt;=x&lt;1.0.  
     */
    ScalingType?: HyperParameterScalingType;
  }
  export interface ContinuousParameterRangeSpecification {
    /**
     * The minimum floating-point value allowed.
     */
    MinValue: ParameterValue;
    /**
     * The maximum floating-point value allowed.
     */
    MaxValue: ParameterValue;
  }
  export type ContinuousParameterRanges = ContinuousParameterRange[];
  export interface ConvergenceDetected {
    /**
     * A flag to stop a tuning job once AMT has detected that the job has converged.
     */
    CompleteOnConvergence?: CompleteOnConvergence;
  }
  export type CountryCode = string;
  export interface CreateActionRequest {
    /**
     * The name of the action. Must be unique to your account in an Amazon Web Services Region.
     */
    ActionName: ExperimentEntityName;
    /**
     * The source type, ID, and URI.
     */
    Source: ActionSource;
    /**
     * The action type.
     */
    ActionType: String256;
    /**
     * The description of the action.
     */
    Description?: ExperimentDescription;
    /**
     * The status of the action.
     */
    Status?: ActionStatus;
    /**
     * A list of properties to add to the action.
     */
    Properties?: LineageEntityParameters;
    MetadataProperties?: MetadataProperties;
    /**
     * A list of tags to apply to the action.
     */
    Tags?: TagList;
  }
  export interface CreateActionResponse {
    /**
     * The Amazon Resource Name (ARN) of the action.
     */
    ActionArn?: ActionArn;
  }
  export interface CreateAlgorithmInput {
    /**
     * The name of the algorithm.
     */
    AlgorithmName: EntityName;
    /**
     * A description of the algorithm.
     */
    AlgorithmDescription?: EntityDescription;
    /**
     * Specifies details about training jobs run by this algorithm, including the following:   The Amazon ECR path of the container and the version digest of the algorithm.   The hyperparameters that the algorithm supports.   The instance types that the algorithm supports for training.   Whether the algorithm supports distributed training.   The metrics that the algorithm emits to Amazon CloudWatch.   Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.   The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.  
     */
    TrainingSpecification: TrainingSpecification;
    /**
     * Specifies details about inference jobs that the algorithm runs, including the following:   The Amazon ECR paths of containers that contain the inference code and model artifacts.   The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.   The input and output content formats that the algorithm supports for inference.  
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * Specifies configurations for one or more training jobs and that SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that SageMaker runs to test the algorithm's inference code.
     */
    ValidationSpecification?: AlgorithmValidationSpecification;
    /**
     * Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
     */
    CertifyForMarketplace?: CertifyForMarketplace;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
  }
  export interface CreateAlgorithmOutput {
    /**
     * The Amazon Resource Name (ARN) of the new algorithm.
     */
    AlgorithmArn: AlgorithmArn;
  }
  export interface CreateAppImageConfigRequest {
    /**
     * The name of the AppImageConfig. Must be unique to your account.
     */
    AppImageConfigName: AppImageConfigName;
    /**
     * A list of tags to apply to the AppImageConfig.
     */
    Tags?: TagList;
    /**
     * The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.
     */
    KernelGatewayImageConfig?: KernelGatewayImageConfig;
    /**
     * The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.
     */
    JupyterLabAppImageConfig?: JupyterLabAppImageConfig;
    /**
     * The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.
     */
    CodeEditorAppImageConfig?: CodeEditorAppImageConfig;
  }
  export interface CreateAppImageConfigResponse {
    /**
     * The ARN of the AppImageConfig.
     */
    AppImageConfigArn?: AppImageConfigArn;
  }
  export interface CreateAppRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name. If this value is not set, then SpaceName must be set.
     */
    UserProfileName?: UserProfileName;
    /**
     * The name of the space. If this value is not set, then UserProfileName must be set.
     */
    SpaceName?: SpaceName;
    /**
     * The type of app.
     */
    AppType: AppType;
    /**
     * The name of the app.
     */
    AppName: AppName;
    /**
     * Each tag consists of a key and an optional value. Tag keys must be unique per resource.
     */
    Tags?: TagList;
    /**
     * The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.  The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error. 
     */
    ResourceSpec?: ResourceSpec;
  }
  export interface CreateAppResponse {
    /**
     * The Amazon Resource Name (ARN) of the app.
     */
    AppArn?: AppArn;
  }
  export interface CreateArtifactRequest {
    /**
     * The name of the artifact. Must be unique to your account in an Amazon Web Services Region.
     */
    ArtifactName?: ExperimentEntityName;
    /**
     * The ID, ID type, and URI of the source.
     */
    Source: ArtifactSource;
    /**
     * The artifact type.
     */
    ArtifactType: String256;
    /**
     * A list of properties to add to the artifact.
     */
    Properties?: ArtifactProperties;
    MetadataProperties?: MetadataProperties;
    /**
     * A list of tags to apply to the artifact.
     */
    Tags?: TagList;
  }
  export interface CreateArtifactResponse {
    /**
     * The Amazon Resource Name (ARN) of the artifact.
     */
    ArtifactArn?: ArtifactArn;
  }
  export interface CreateAutoMLJobRequest {
    /**
     * Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
     */
    InputDataConfig: AutoMLInputDataConfig;
    /**
     * Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
     */
    OutputDataConfig: AutoMLOutputDataConfig;
    /**
     * Defines the type of supervised learning problem available for the candidates. For more information, see  SageMaker Autopilot problem types.
     */
    ProblemType?: ProblemType;
    /**
     * Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
     */
    AutoMLJobObjective?: AutoMLJobObjective;
    /**
     * A collection of settings used to configure an AutoML job.
     */
    AutoMLJobConfig?: AutoMLJobConfig;
    /**
     * The ARN of the role that is used to access the data.
     */
    RoleArn: RoleArn;
    /**
     * Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
     */
    GenerateCandidateDefinitionsOnly?: GenerateCandidateDefinitionsOnly;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
     */
    Tags?: TagList;
    /**
     * Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
     */
    ModelDeployConfig?: ModelDeployConfig;
  }
  export interface CreateAutoMLJobResponse {
    /**
     * The unique ARN assigned to the AutoML job when it is created.
     */
    AutoMLJobArn: AutoMLJobArn;
  }
  export interface CreateAutoMLJobV2Request {
    /**
     * Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:   For tabular problem types: S3Prefix, ManifestFile.   For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.   For text classification: S3Prefix.   For time-series forecasting: S3Prefix.   For text generation (LLMs fine-tuning): S3Prefix.  
     */
    AutoMLJobInputDataConfig: AutoMLJobInputDataConfig;
    /**
     * Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
     */
    OutputDataConfig: AutoMLOutputDataConfig;
    /**
     * Defines the configuration settings of one of the supported problem types.
     */
    AutoMLProblemTypeConfig: AutoMLProblemTypeConfig;
    /**
     * The ARN of the role that is used to access the data.
     */
    RoleArn: RoleArn;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
     */
    Tags?: TagList;
    /**
     * The security configuration for traffic encryption or Amazon VPC settings.
     */
    SecurityConfig?: AutoMLSecurityConfig;
    /**
     * Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.    For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.   For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.   
     */
    AutoMLJobObjective?: AutoMLJobObjective;
    /**
     * Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
     */
    ModelDeployConfig?: ModelDeployConfig;
    /**
     * This structure specifies how to split the data into train and validation datasets. The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.  This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets. 
     */
    DataSplitConfig?: AutoMLDataSplitConfig;
    /**
     * Specifies the compute configuration for the AutoML job V2.
     */
    AutoMLComputeConfig?: AutoMLComputeConfig;
  }
  export interface CreateAutoMLJobV2Response {
    /**
     * The unique ARN assigned to the AutoMLJob when it is created.
     */
    AutoMLJobArn: AutoMLJobArn;
  }
  export interface CreateClusterRequest {
    /**
     * The name for the new SageMaker HyperPod cluster.
     */
    ClusterName: ClusterName;
    /**
     * The instance groups to be created in the SageMaker HyperPod cluster.
     */
    InstanceGroups: ClusterInstanceGroupSpecifications;
    VpcConfig?: VpcConfig;
    /**
     * Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateClusterResponse {
    /**
     * The Amazon Resource Name (ARN) of the cluster.
     */
    ClusterArn: ClusterArn;
  }
  export interface CreateCodeRepositoryInput {
    /**
     * The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
     */
    CodeRepositoryName: EntityName;
    /**
     * Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.
     */
    GitConfig: GitConfig;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
  }
  export interface CreateCodeRepositoryOutput {
    /**
     * The Amazon Resource Name (ARN) of the new repository.
     */
    CodeRepositoryArn: CodeRepositoryArn;
  }
  export interface CreateCompilationJobRequest {
    /**
     * A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. 
     */
    CompilationJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.  During model compilation, Amazon SageMaker needs your permission to:   Read input data from an S3 bucket   Write model artifacts to an S3 bucket   Write logs to Amazon CloudWatch Logs   Publish metrics to Amazon CloudWatch   You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles. 
     */
    RoleArn: RoleArn;
    /**
     * The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.
     */
    ModelPackageVersionArn?: ModelPackageArn;
    /**
     * Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
     */
    InputConfig?: InputConfig;
    /**
     * Provides information about the output location for the compiled model and the target device the model runs on.
     */
    OutputConfig: OutputConfig;
    /**
     * A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: NeoVpcConfig;
    /**
     * Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
     */
    StoppingCondition: StoppingCondition;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
  }
  export interface CreateCompilationJobResponse {
    /**
     * If the action is successful, the service sends back an HTTP 200 response. Amazon SageMaker returns the following data in JSON format:    CompilationJobArn: The Amazon Resource Name (ARN) of the compiled job.  
     */
    CompilationJobArn: CompilationJobArn;
  }
  export interface CreateContextRequest {
    /**
     * The name of the context. Must be unique to your account in an Amazon Web Services Region.
     */
    ContextName: ContextName;
    /**
     * The source type, ID, and URI.
     */
    Source: ContextSource;
    /**
     * The context type.
     */
    ContextType: String256;
    /**
     * The description of the context.
     */
    Description?: ExperimentDescription;
    /**
     * A list of properties to add to the context.
     */
    Properties?: LineageEntityParameters;
    /**
     * A list of tags to apply to the context.
     */
    Tags?: TagList;
  }
  export interface CreateContextResponse {
    /**
     * The Amazon Resource Name (ARN) of the context.
     */
    ContextArn?: ContextArn;
  }
  export interface CreateDataQualityJobDefinitionRequest {
    /**
     * The name for the monitoring job definition.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * Configures the constraints and baselines for the monitoring job.
     */
    DataQualityBaselineConfig?: DataQualityBaselineConfig;
    /**
     * Specifies the container that runs the monitoring job.
     */
    DataQualityAppSpecification: DataQualityAppSpecification;
    /**
     * A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.
     */
    DataQualityJobInput: DataQualityJobInput;
    DataQualityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Specifies networking configuration for the monitoring job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
    /**
     * (Optional) An array of key-value pairs. For more information, see  Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateDataQualityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the job definition.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
  }
  export interface CreateDeviceFleetRequest {
    /**
     * The name of the fleet that the device belongs to.
     */
    DeviceFleetName: EntityName;
    /**
     * The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
     */
    RoleArn?: RoleArn;
    /**
     * A description of the fleet.
     */
    Description?: DeviceFleetDescription;
    /**
     * The output configuration for storing sample data collected by the fleet.
     */
    OutputConfig: EdgeOutputConfig;
    /**
     * Creates tags for the specified fleet.
     */
    Tags?: TagList;
    /**
     * Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
     */
    EnableIotRoleAlias?: EnableIotRoleAlias;
  }
  export interface CreateDomainRequest {
    /**
     * A name for the domain.
     */
    DomainName: DomainName;
    /**
     * The mode of authentication that members use to access the domain.
     */
    AuthMode: AuthMode;
    /**
     * The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.  SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.
     */
    DefaultUserSettings: UserSettings;
    /**
     * A collection of Domain settings.
     */
    DomainSettings?: DomainSettings;
    /**
     * The VPC subnets that the domain uses for communication.
     */
    SubnetIds: Subnets;
    /**
     * The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
     */
    VpcId: VpcId;
    /**
     * Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. Tags that you specify for the Domain are also added to all Apps that the Domain launches.
     */
    Tags?: TagList;
    /**
     * Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.    PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access    VpcOnly - All traffic is through the specified VPC and subnets  
     */
    AppNetworkAccessType?: AppNetworkAccessType;
    /**
     * Use KmsKeyId.
     */
    HomeEfsFileSystemKmsKeyId?: KmsKeyId;
    /**
     * SageMaker uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
     */
    AppSecurityGroupManagement?: AppSecurityGroupManagement;
    /**
     * The default settings used to create a space.
     */
    DefaultSpaceSettings?: DefaultSpaceSettings;
  }
  export interface CreateDomainResponse {
    /**
     * The Amazon Resource Name (ARN) of the created domain.
     */
    DomainArn?: DomainArn;
    /**
     * The URL to the created domain.
     */
    Url?: String1024;
  }
  export interface CreateEdgeDeploymentPlanRequest {
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * List of models associated with the edge deployment plan.
     */
    ModelConfigs: EdgeDeploymentModelConfigs;
    /**
     * The device fleet used for this edge deployment plan.
     */
    DeviceFleetName: EntityName;
    /**
     * List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.
     */
    Stages?: DeploymentStages;
    /**
     * List of tags with which to tag the edge deployment plan.
     */
    Tags?: TagList;
  }
  export interface CreateEdgeDeploymentPlanResponse {
    /**
     * The ARN of the edge deployment plan.
     */
    EdgeDeploymentPlanArn: EdgeDeploymentPlanArn;
  }
  export interface CreateEdgeDeploymentStageRequest {
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * List of stages to be added to the edge deployment plan.
     */
    Stages: DeploymentStages;
  }
  export interface CreateEdgePackagingJobRequest {
    /**
     * The name of the edge packaging job.
     */
    EdgePackagingJobName: EntityName;
    /**
     * The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.
     */
    CompilationJobName: EntityName;
    /**
     * The name of the model.
     */
    ModelName: EntityName;
    /**
     * The version of the model.
     */
    ModelVersion: EdgeVersion;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.
     */
    RoleArn: RoleArn;
    /**
     * Provides information about the output location for the packaged model.
     */
    OutputConfig: EdgeOutputConfig;
    /**
     * The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.
     */
    ResourceKey?: KmsKeyId;
    /**
     * Creates tags for the packaging job.
     */
    Tags?: TagList;
  }
  export interface CreateEndpointConfigInput {
    /**
     * The name of the endpoint configuration. You specify this name in a CreateEndpoint request. 
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
     */
    ProductionVariants: ProductionVariantList;
    DataCaptureConfig?: DataCaptureConfig;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKeyId can be any of the following formats:    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias    The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint, UpdateEndpoint requests. For more information, refer to the Amazon Web Services Key Management Service section Using Key Policies in Amazon Web Services KMS    Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes. 
     */
    KmsKeyId?: KmsKeyId;
    /**
     * Specifies configuration for how an endpoint performs asynchronous inference. This is a required field in order for your Endpoint to be invoked using InvokeEndpointAsync.
     */
    AsyncInferenceConfig?: AsyncInferenceConfig;
    /**
     * A member of CreateEndpointConfig that enables explainers.
     */
    ExplainerConfig?: ExplainerConfig;
    /**
     * An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants. If you use this field, you can only specify one variant for ProductionVariants and one variant for ShadowProductionVariants.
     */
    ShadowProductionVariants?: ProductionVariantList;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform actions on your behalf. For more information, see SageMaker Roles.   To be able to pass this role to Amazon SageMaker, the caller of this action must have the iam:PassRole permission. 
     */
    ExecutionRoleArn?: RoleArn;
    VpcConfig?: VpcConfig;
    /**
     * Sets whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
     */
    EnableNetworkIsolation?: Boolean;
  }
  export interface CreateEndpointConfigOutput {
    /**
     * The Amazon Resource Name (ARN) of the endpoint configuration. 
     */
    EndpointConfigArn: EndpointConfigArn;
  }
  export interface CreateEndpointInput {
    /**
     * The name of the endpoint.The name must be unique within an Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.
     */
    EndpointName: EndpointName;
    /**
     * The name of an endpoint configuration. For more information, see CreateEndpointConfig. 
     */
    EndpointConfigName: EndpointConfigName;
    DeploymentConfig?: DeploymentConfig;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
  }
  export interface CreateEndpointOutput {
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
  }
  export interface CreateExperimentRequest {
    /**
     * The name of the experiment. The name must be unique in your Amazon Web Services account and is not case-sensitive.
     */
    ExperimentName: ExperimentEntityName;
    /**
     * The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName, the value in ExperimentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The description of the experiment.
     */
    Description?: ExperimentDescription;
    /**
     * A list of tags to associate with the experiment. You can use Search API to search on the tags.
     */
    Tags?: TagList;
  }
  export interface CreateExperimentResponse {
    /**
     * The Amazon Resource Name (ARN) of the experiment.
     */
    ExperimentArn?: ExperimentArn;
  }
  export interface CreateFeatureGroupRequest {
    /**
     * The name of the FeatureGroup. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. The name:   Must start with an alphanumeric character.   Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.  
     */
    FeatureGroupName: FeatureGroupName;
    /**
     * The name of the Feature whose value uniquely identifies a Record defined in the FeatureStore. Only the latest record per identifier value will be stored in the OnlineStore. RecordIdentifierFeatureName must be one of feature definitions' names. You use the RecordIdentifierFeatureName to access data in a FeatureStore. This name:   Must start with an alphanumeric character.   Can only contains alphanumeric characters, hyphens, underscores. Spaces are not allowed.   
     */
    RecordIdentifierFeatureName: FeatureName;
    /**
     * The name of the feature that stores the EventTime of a Record in a FeatureGroup. An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime. An EventTime can be a String or Fractional.     Fractional: EventTime feature values must be a Unix timestamp in seconds.    String: EventTime feature values must be an ISO-8601 string in the format. The following formats are supported yyyy-MM-dd'T'HH:mm:ssZ and yyyy-MM-dd'T'HH:mm:ss.SSSZ where yyyy, MM, and dd represent the year, month, and day respectively and HH, mm, ss, and if applicable, SSS represent the hour, month, second and milliseconds respsectively. 'T' and Z are constants.  
     */
    EventTimeFeatureName: FeatureName;
    /**
     * A list of Feature names and types. Name and Type is compulsory per Feature.  Valid feature FeatureTypes are Integral, Fractional and String.  FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time  You can create up to 2,500 FeatureDefinitions per FeatureGroup.
     */
    FeatureDefinitions: FeatureDefinitions;
    /**
     * You can turn the OnlineStore on or off by specifying True for the EnableOnlineStore flag in OnlineStoreConfig. You can also include an Amazon Web Services KMS key ID (KMSKeyId) for at-rest encryption of the OnlineStore. The default value is False.
     */
    OnlineStoreConfig?: OnlineStoreConfig;
    /**
     * Use this to configure an OfflineFeatureStore. This parameter allows you to specify:   The Amazon Simple Storage Service (Amazon S3) location of an OfflineStore.   A configuration for an Amazon Web Services Glue or Amazon Web Services Hive data catalog.    An KMS encryption key to encrypt the Amazon S3 location used for OfflineStore. If KMS encryption key is not specified, by default we encrypt all data at rest using Amazon Web Services KMS key. By defining your bucket-level key for SSE, you can reduce Amazon Web Services KMS requests costs by up to 99 percent.   Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.   To learn more about this parameter, see OfflineStoreConfig.
     */
    OfflineStoreConfig?: OfflineStoreConfig;
    ThroughputConfig?: ThroughputConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
     */
    RoleArn?: RoleArn;
    /**
     * A free-form description of a FeatureGroup.
     */
    Description?: Description;
    /**
     * Tags used to identify Features in each FeatureGroup.
     */
    Tags?: TagList;
  }
  export interface CreateFeatureGroupResponse {
    /**
     * The Amazon Resource Name (ARN) of the FeatureGroup. This is a unique identifier for the feature group. 
     */
    FeatureGroupArn: FeatureGroupArn;
  }
  export interface CreateFlowDefinitionRequest {
    /**
     * The name of your flow definition.
     */
    FlowDefinitionName: FlowDefinitionName;
    /**
     * Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
     */
    HumanLoopRequestSource?: HumanLoopRequestSource;
    /**
     * An object containing information about the events that trigger a human workflow.
     */
    HumanLoopActivationConfig?: HumanLoopActivationConfig;
    /**
     * An object containing information about the tasks the human reviewers will perform.
     */
    HumanLoopConfig?: HumanLoopConfig;
    /**
     * An object containing information about where the human review results will be uploaded.
     */
    OutputConfig: FlowDefinitionOutputConfig;
    /**
     * The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.
     */
    RoleArn: RoleArn;
    /**
     * An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.
     */
    Tags?: TagList;
  }
  export interface CreateFlowDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the flow definition you create.
     */
    FlowDefinitionArn: FlowDefinitionArn;
  }
  export interface CreateHubContentReferenceRequest {
    /**
     * The name of the hub to add the hub content reference to.
     */
    HubName: HubNameOrArn;
    /**
     * The ARN of the public hub content to reference.
     */
    SageMakerPublicHubContentArn: SageMakerPublicHubContentArn;
    /**
     * The name of the hub content to reference.
     */
    HubContentName?: HubContentName;
    /**
     * The minimum version of the hub content to reference.
     */
    MinVersion?: HubContentVersion;
    /**
     * Any tags associated with the hub content to reference.
     */
    Tags?: TagList;
  }
  export interface CreateHubContentReferenceResponse {
    /**
     * The ARN of the hub that the hub content reference was added to.
     */
    HubArn: HubArn;
    /**
     * The ARN of the hub content.
     */
    HubContentArn: HubContentArn;
  }
  export interface CreateHubRequest {
    /**
     * The name of the hub to create.
     */
    HubName: HubName;
    /**
     * A description of the hub.
     */
    HubDescription: HubDescription;
    /**
     * The display name of the hub.
     */
    HubDisplayName?: HubDisplayName;
    /**
     * The searchable keywords for the hub.
     */
    HubSearchKeywords?: HubSearchKeywordList;
    /**
     * The Amazon S3 storage configuration for the hub.
     */
    S3StorageConfig?: HubS3StorageConfig;
    /**
     * Any tags to associate with the hub.
     */
    Tags?: TagList;
  }
  export interface CreateHubResponse {
    /**
     * The Amazon Resource Name (ARN) of the hub.
     */
    HubArn: HubArn;
  }
  export interface CreateHumanTaskUiRequest {
    /**
     * The name of the user interface you are creating.
     */
    HumanTaskUiName: HumanTaskUiName;
    UiTemplate: UiTemplate;
    /**
     * An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.
     */
    Tags?: TagList;
  }
  export interface CreateHumanTaskUiResponse {
    /**
     * The Amazon Resource Name (ARN) of the human review workflow user interface you create.
     */
    HumanTaskUiArn: HumanTaskUiArn;
  }
  export interface CreateHyperParameterTuningJobRequest {
    /**
     * The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same Amazon Web Services account and Amazon Web Services Region. The name must have 1 to 32 characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
    /**
     * The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see How Hyperparameter Tuning Works.
     */
    HyperParameterTuningJobConfig: HyperParameterTuningJobConfig;
    /**
     * The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.
     */
    TrainingJobDefinition?: HyperParameterTrainingJobDefinition;
    /**
     * A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
     */
    TrainingJobDefinitions?: HyperParameterTrainingJobDefinitions;
    /**
     * Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job. All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.  All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job. 
     */
    WarmStartConfig?: HyperParameterTuningJobWarmStartConfig;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.
     */
    Tags?: TagList;
    /**
     * Configures SageMaker Automatic model tuning (AMT) to automatically find optimal parameters for the following fields:    ParameterRanges: The names and ranges of parameters that a hyperparameter tuning job can optimize.    ResourceLimits: The maximum resources that can be used for a training job. These resources include the maximum number of training jobs, the maximum runtime of a tuning job, and the maximum number of training jobs to run at the same time.    TrainingJobEarlyStoppingType: A flag that specifies whether or not to use early stopping for training jobs launched by a hyperparameter tuning job.    RetryStrategy: The number of times to retry a training job.    Strategy: Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training jobs that it launches.    ConvergenceDetected: A flag to indicate that Automatic model tuning (AMT) has detected model convergence.  
     */
    Autotune?: Autotune;
  }
  export interface CreateHyperParameterTuningJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the tuning job. SageMaker assigns an ARN to a hyperparameter tuning job when you create it.
     */
    HyperParameterTuningJobArn: HyperParameterTuningJobArn;
  }
  export interface CreateImageRequest {
    /**
     * The description of the image.
     */
    Description?: ImageDescription;
    /**
     * The display name of the image. If not provided, ImageName is displayed.
     */
    DisplayName?: ImageDisplayName;
    /**
     * The name of the image. Must be unique to your account.
     */
    ImageName: ImageName;
    /**
     * The ARN of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    /**
     * A list of tags to apply to the image.
     */
    Tags?: TagList;
  }
  export interface CreateImageResponse {
    /**
     * The ARN of the image.
     */
    ImageArn?: ImageArn;
  }
  export interface CreateImageVersionRequest {
    /**
     * The registry path of the container image to use as the starting point for this version. The path is an Amazon ECR URI in the following format:  &lt;acct-id&gt;.dkr.ecr.&lt;region&gt;.amazonaws.com/&lt;repo-name[:tag] or [@digest]&gt; 
     */
    BaseImage: ImageBaseImage;
    /**
     * A unique ID. If not specified, the Amazon Web Services CLI and Amazon Web Services SDKs, such as the SDK for Python (Boto3), add a unique value to the call.
     */
    ClientToken: ClientToken;
    /**
     * The ImageName of the Image to create a version of.
     */
    ImageName: ImageName;
    /**
     * A list of aliases created with the image version.
     */
    Aliases?: SageMakerImageVersionAliases;
    /**
     * The stability of the image version, specified by the maintainer.    NOT_PROVIDED: The maintainers did not provide a status for image version stability.    STABLE: The image version is stable.    TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.    ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.   
     */
    VendorGuidance?: VendorGuidance;
    /**
     * Indicates SageMaker job type compatibility.    TRAINING: The image version is compatible with SageMaker training jobs.    INFERENCE: The image version is compatible with SageMaker inference jobs.    NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.  
     */
    JobType?: JobType;
    /**
     * The machine learning framework vended in the image version.
     */
    MLFramework?: MLFramework;
    /**
     * The supported programming language and its version.
     */
    ProgrammingLang?: ProgrammingLang;
    /**
     * Indicates CPU or GPU compatibility.    CPU: The image version is compatible with CPU.    GPU: The image version is compatible with GPU.  
     */
    Processor?: Processor;
    /**
     * Indicates Horovod compatibility.
     */
    Horovod?: Horovod;
    /**
     * The maintainer description of the image version.
     */
    ReleaseNotes?: ReleaseNotes;
  }
  export interface CreateImageVersionResponse {
    /**
     * The ARN of the image version.
     */
    ImageVersionArn?: ImageVersionArn;
  }
  export interface CreateInferenceComponentInput {
    /**
     * A unique name to assign to the inference component.
     */
    InferenceComponentName: InferenceComponentName;
    /**
     * The name of an existing endpoint where you host the inference component.
     */
    EndpointName: EndpointName;
    /**
     * The name of an existing production variant where you host the inference component.
     */
    VariantName: VariantName;
    /**
     * Details about the resources to deploy with this inference component, including the model, container, and compute resources.
     */
    Specification: InferenceComponentSpecification;
    /**
     * Runtime settings for a model that is deployed with an inference component.
     */
    RuntimeConfig: InferenceComponentRuntimeConfig;
    /**
     * A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference.
     */
    Tags?: TagList;
  }
  export interface CreateInferenceComponentOutput {
    /**
     * The Amazon Resource Name (ARN) of the inference component.
     */
    InferenceComponentArn: InferenceComponentArn;
  }
  export interface CreateInferenceExperimentRequest {
    /**
     * The name for the inference experiment.
     */
    Name: InferenceExperimentName;
    /**
     *  The type of the inference experiment that you want to run. The following types of experiments are possible:     ShadowMode: You can use this type to validate a shadow variant. For more information, see Shadow tests.   
     */
    Type: InferenceExperimentType;
    /**
     *  The duration for which you want the inference experiment to run. If you don't specify this field, the experiment automatically starts immediately upon creation and concludes after 7 days. 
     */
    Schedule?: InferenceExperimentSchedule;
    /**
     * A description for the inference experiment.
     */
    Description?: InferenceExperimentDescription;
    /**
     *  The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. 
     */
    RoleArn: RoleArn;
    /**
     *  The name of the Amazon SageMaker endpoint on which you want to run the inference experiment. 
     */
    EndpointName: EndpointName;
    /**
     *  An array of ModelVariantConfig objects. There is one for each variant in the inference experiment. Each ModelVariantConfig object in the array describes the infrastructure configuration for the corresponding variant. 
     */
    ModelVariants: ModelVariantConfigList;
    /**
     *  The Amazon S3 location and configuration for storing inference request and response data.   This is an optional parameter that you can use for data capture. For more information, see Capture data. 
     */
    DataStorageConfig?: InferenceExperimentDataStorageConfig;
    /**
     *  The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates. 
     */
    ShadowModeConfig: ShadowModeConfig;
    /**
     *  The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. The KmsKey can be any of the following formats:    KMS key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    Amazon Resource Name (ARN) of a KMS key  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"    KMS key Alias  "alias/ExampleAlias"    Amazon Resource Name (ARN) of a KMS key Alias  "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"     If you use a KMS key ID or an alias of your KMS key, the Amazon SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. Amazon SageMaker uses server-side encryption with KMS managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.   The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide. 
     */
    KmsKey?: KmsKeyId;
    /**
     *  Array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging your Amazon Web Services Resources. 
     */
    Tags?: TagList;
  }
  export interface CreateInferenceExperimentResponse {
    /**
     * The ARN for your inference experiment.
     */
    InferenceExperimentArn: InferenceExperimentArn;
  }
  export interface CreateInferenceRecommendationsJobRequest {
    /**
     * A name for the recommendation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account. The job name is passed down to the resources created by the recommendation job. The names of resources (such as the model, endpoint configuration, endpoint, and compilation) that are prefixed with the job name are truncated at 40 characters.
     */
    JobName: RecommendationJobName;
    /**
     * Defines the type of recommendation job. Specify Default to initiate an instance recommendation and Advanced to initiate a load test. If left unspecified, Amazon SageMaker Inference Recommender will run an instance recommendation (DEFAULT) job.
     */
    JobType: RecommendationJobType;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    /**
     * Provides information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations.
     */
    InputConfig: RecommendationJobInputConfig;
    /**
     * Description of the recommendation job.
     */
    JobDescription?: RecommendationJobDescription;
    /**
     * A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped.
     */
    StoppingConditions?: RecommendationJobStoppingConditions;
    /**
     * Provides information about the output artifacts and the KMS key to use for Amazon S3 server-side encryption.
     */
    OutputConfig?: RecommendationJobOutputConfig;
    /**
     * The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.
     */
    Tags?: TagList;
  }
  export interface CreateInferenceRecommendationsJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the recommendation job.
     */
    JobArn: RecommendationJobArn;
  }
  export interface CreateLabelingJobRequest {
    /**
     * The name of the labeling job. This name is used to identify the job in a list of labeling jobs. Labeling job names must be unique within an Amazon Web Services account and region. LabelingJobName is not case sensitive. For example, Example-job and example-job are considered the same labeling job name by Ground Truth.
     */
    LabelingJobName: LabelingJobName;
    /**
     * The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The LabelAttributeName must meet the following requirements.   The name can't end with "-metadata".    If you are using one of the following built-in task types, the attribute name must end with "-ref". If the task type you are using is not listed below, the attribute name must not end with "-ref".   Image semantic segmentation (SemanticSegmentation), and adjustment (AdjustmentSemanticSegmentation) and verification (VerificationSemanticSegmentation) labeling jobs for this task type.   Video frame object detection (VideoObjectDetection), and adjustment and verification (AdjustmentVideoObjectDetection) labeling jobs for this task type.   Video frame object tracking (VideoObjectTracking), and adjustment and verification (AdjustmentVideoObjectTracking) labeling jobs for this task type.   3D point cloud semantic segmentation (3DPointCloudSemanticSegmentation), and adjustment and verification (Adjustment3DPointCloudSemanticSegmentation) labeling jobs for this task type.    3D point cloud object tracking (3DPointCloudObjectTracking), and adjustment and verification (Adjustment3DPointCloudObjectTracking) labeling jobs for this task type.        If you are creating an adjustment or verification labeling job, you must use a different LabelAttributeName than the one used in the original labeling job. The original labeling job is the Ground Truth labeling job that produced the labels that you want verified or adjusted. To learn more about adjustment and verification labeling jobs, see Verify and Adjust Labels. 
     */
    LabelAttributeName: LabelAttributeName;
    /**
     * Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects. You must specify at least one of the following: S3DataSource or SnsDataSource.    Use SnsDataSource to specify an SNS input topic for a streaming labeling job. If you do not specify and SNS input topic ARN, Ground Truth will create a one-time labeling job that stops after all data objects in the input manifest file have been labeled.   Use S3DataSource to specify an input manifest file for both streaming and one-time labeling jobs. Adding an S3DataSource is optional if you use SnsDataSource to create a streaming labeling job.   If you use the Amazon Mechanical Turk workforce, your input data should not include confidential information, personal information or protected health information. Use ContentClassifiers to specify that your data is free of personally identifiable information and adult content.
     */
    InputConfig: LabelingJobInputConfig;
    /**
     * The location of the output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
     */
    OutputConfig: LabelingJobOutputConfig;
    /**
     * The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.
     */
    RoleArn: RoleArn;
    /**
     * The S3 URI of the file, referred to as a label category configuration file, that defines the categories used to label the data objects. For 3D point cloud and video frame task types, you can add label category attributes and frame attributes to your label category configuration file. To learn how, see Create a Labeling Category Configuration File for 3D Point Cloud Labeling Jobs.  For named entity recognition jobs, in addition to "labels", you must provide worker instructions in the label category configuration file using the "instructions" parameter: "instructions": {"shortInstruction":"&lt;h1&gt;Add header&lt;/h1&gt;&lt;p&gt;Add Instructions&lt;/p&gt;", "fullInstruction":"&lt;p&gt;Add additional instructions.&lt;/p&gt;"}. For details and an example, see Create a Named Entity Recognition Labeling Job (API) . For all other built-in task types and custom tasks, your label category configuration file must be a JSON file in the following format. Identify the labels you want to use by replacing label_1, label_2,...,label_n with your label categories.  {    "document-version": "2018-11-28",   "labels": [{"label": "label_1"},{"label": "label_2"},...{"label": "label_n"}]   }  Note the following about the label category configuration file:   For image classification and text classification (single and multi-label) you must specify at least two label categories. For all other task types, the minimum number of label categories required is one.    Each label category must be unique, you cannot specify duplicate label categories.   If you create a 3D point cloud or video frame adjustment or verification labeling job, you must include auditLabelAttributeName in the label category configuration. Use this parameter to enter the  LabelAttributeName  of the labeling job you want to adjust or verify annotations of.  
     */
    LabelCategoryConfigS3Uri?: S3Uri;
    /**
     * A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.
     */
    StoppingConditions?: LabelingJobStoppingConditions;
    /**
     * Configures the information required to perform automated data labeling.
     */
    LabelingJobAlgorithmsConfig?: LabelingJobAlgorithmsConfig;
    /**
     * Configures the labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).
     */
    HumanTaskConfig: HumanTaskConfig;
    /**
     * An array of key/value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateLabelingJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the labeling job. You use this ARN to identify the labeling job.
     */
    LabelingJobArn: LabelingJobArn;
  }
  export interface CreateMlflowTrackingServerRequest {
    /**
     * A unique string identifying the tracking server name. This string is part of the tracking server ARN.
     */
    TrackingServerName: TrackingServerName;
    /**
     * The S3 URI for a general purpose bucket to use as the MLflow Tracking Server artifact store.
     */
    ArtifactStoreUri: S3Uri;
    /**
     * The size of the tracking server you want to create. You can choose between "Small", "Medium", and "Large". The default MLflow Tracking Server configuration size is "Small". You can choose a size depending on the projected use of the tracking server such as the volume of data logged, number of users, and frequency of use.  We recommend using a small tracking server for teams of up to 25 users, a medium tracking server for teams of up to 50 users, and a large tracking server for teams of up to 100 users. 
     */
    TrackingServerSize?: TrackingServerSize;
    /**
     * The version of MLflow that the tracking server uses. To see which MLflow versions are available to use, see How it works.
     */
    MlflowVersion?: MlflowVersion;
    /**
     * The Amazon Resource Name (ARN) for an IAM role in your account that the MLflow Tracking Server uses to access the artifact store in Amazon S3. The role should have AmazonS3FullAccess permissions. For more information on IAM permissions for tracking server creation, see Set up IAM permissions for MLflow.
     */
    RoleArn: RoleArn;
    /**
     * Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False.
     */
    AutomaticModelRegistration?: Boolean;
    /**
     * The day and time of the week in Coordinated Universal Time (UTC) 24-hour standard time that weekly maintenance updates are scheduled. For example: TUE:03:30.
     */
    WeeklyMaintenanceWindowStart?: WeeklyMaintenanceWindowStart;
    /**
     * Tags consisting of key-value pairs used to manage metadata for the tracking server.
     */
    Tags?: TagList;
  }
  export interface CreateMlflowTrackingServerResponse {
    /**
     * The ARN of the tracking server.
     */
    TrackingServerArn?: TrackingServerArn;
  }
  export interface CreateModelBiasJobDefinitionRequest {
    /**
     * The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The baseline configuration for a model bias job.
     */
    ModelBiasBaselineConfig?: ModelBiasBaselineConfig;
    /**
     * Configures the model bias job to run a specified Docker container image.
     */
    ModelBiasAppSpecification: ModelBiasAppSpecification;
    /**
     * Inputs for the model bias job.
     */
    ModelBiasJobInput: ModelBiasJobInput;
    ModelBiasJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Networking options for a model bias job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
    /**
     * (Optional) An array of key-value pairs. For more information, see  Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateModelBiasJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model bias job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
  }
  export interface CreateModelCardExportJobRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the model card to export.
     */
    ModelCardName: ModelCardNameOrArn;
    /**
     * The version of the model card to export. If a version is not provided, then the latest version of the model card is exported.
     */
    ModelCardVersion?: Integer;
    /**
     * The name of the model card export job.
     */
    ModelCardExportJobName: EntityName;
    /**
     * The model card output configuration that specifies the Amazon S3 path for exporting.
     */
    OutputConfig: ModelCardExportOutputConfig;
  }
  export interface CreateModelCardExportJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the model card export job.
     */
    ModelCardExportJobArn: ModelCardExportJobArn;
  }
  export interface CreateModelCardRequest {
    /**
     * The unique name of the model card.
     */
    ModelCardName: EntityName;
    /**
     * An optional Key Management Service key to encrypt, decrypt, and re-encrypt model card content for regulated workloads with highly sensitive data.
     */
    SecurityConfig?: ModelCardSecurityConfig;
    /**
     * The content of the model card. Content must be in model card JSON schema and provided as a string.
     */
    Content: ModelCardContent;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus: ModelCardStatus;
    /**
     * Key-value pairs used to manage metadata for model cards.
     */
    Tags?: TagList;
  }
  export interface CreateModelCardResponse {
    /**
     * The Amazon Resource Name (ARN) of the successfully created model card.
     */
    ModelCardArn: ModelCardArn;
  }
  export interface CreateModelExplainabilityJobDefinitionRequest {
    /**
     *  The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The baseline configuration for a model explainability job.
     */
    ModelExplainabilityBaselineConfig?: ModelExplainabilityBaselineConfig;
    /**
     * Configures the model explainability job to run a specified Docker container image.
     */
    ModelExplainabilityAppSpecification: ModelExplainabilityAppSpecification;
    /**
     * Inputs for the model explainability job.
     */
    ModelExplainabilityJobInput: ModelExplainabilityJobInput;
    ModelExplainabilityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Networking options for a model explainability job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
    /**
     * (Optional) An array of key-value pairs. For more information, see  Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateModelExplainabilityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model explainability job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
  }
  export interface CreateModelInput {
    /**
     * The name of the new model.
     */
    ModelName: ModelName;
    /**
     * The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions. 
     */
    PrimaryContainer?: ContainerDefinition;
    /**
     * Specifies the containers in the inference pipeline.
     */
    Containers?: ContainerDefinitionList;
    /**
     * Specifies details of how containers in a multi-container endpoint are called.
     */
    InferenceExecutionConfig?: InferenceExecutionConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see SageMaker Roles.   To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission. 
     */
    ExecutionRoleArn?: RoleArn;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: VpcConfig;
    /**
     * Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
     */
    EnableNetworkIsolation?: Boolean;
  }
  export interface CreateModelOutput {
    /**
     * The ARN of the model created in SageMaker.
     */
    ModelArn: ModelArn;
  }
  export interface CreateModelPackageGroupInput {
    /**
     * The name of the model group.
     */
    ModelPackageGroupName: EntityName;
    /**
     * A description for the model group.
     */
    ModelPackageGroupDescription?: EntityDescription;
    /**
     * A list of key value pairs associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
  }
  export interface CreateModelPackageGroupOutput {
    /**
     * The Amazon Resource Name (ARN) of the model group.
     */
    ModelPackageGroupArn: ModelPackageGroupArn;
  }
  export interface CreateModelPackageInput {
    /**
     * The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen). This parameter is required for unversioned models. It is not applicable to versioned models.
     */
    ModelPackageName?: EntityName;
    /**
     * The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to. This parameter is required for versioned models, and does not apply to unversioned models.
     */
    ModelPackageGroupName?: ArnOrName;
    /**
     * A description of the model package.
     */
    ModelPackageDescription?: EntityDescription;
    /**
     * Specifies details about inference jobs that you can run with models based on this model package, including the following information:   The Amazon ECR paths of containers that contain the inference code and model artifacts.   The instance types that the model package supports for transform jobs and real-time endpoints used for inference.   The input and output content formats that the model package supports for inference.  
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.
     */
    ValidationSpecification?: ModelPackageValidationSpecification;
    /**
     * Details about the algorithm that was used to create the model package.
     */
    SourceAlgorithmSpecification?: SourceAlgorithmSpecification;
    /**
     * Whether to certify the model package for listing on Amazon Web Services Marketplace. This parameter is optional for unversioned models, and does not apply to versioned models.
     */
    CertifyForMarketplace?: CertifyForMarketplace;
    /**
     * A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide. If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument. 
     */
    Tags?: TagList;
    /**
     * Whether the model is approved for deployment. This parameter is optional for versioned models, and does not apply to unversioned models. For versioned models, the value of this parameter must be set to Approved to deploy the model.
     */
    ModelApprovalStatus?: ModelApprovalStatus;
    MetadataProperties?: MetadataProperties;
    /**
     * A structure that contains model metrics reports.
     */
    ModelMetrics?: ModelMetrics;
    /**
     * A unique token that guarantees that the call to this API is idempotent.
     */
    ClientToken?: ClientToken;
    /**
     * The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
     */
    Domain?: String;
    /**
     * The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER". Specify "OTHER" if none of the tasks listed fit your use case.
     */
    Task?: String;
    /**
     * The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.
     */
    SamplePayloadUrl?: S3Uri;
    /**
     * The metadata properties associated with the model package versions.
     */
    CustomerMetadataProperties?: CustomerMetadataMap;
    /**
     * Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide. 
     */
    DriftCheckBaselines?: DriftCheckBaselines;
    /**
     * An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts. 
     */
    AdditionalInferenceSpecifications?: AdditionalInferenceSpecifications;
    /**
     * Indicates if you want to skip model validation.
     */
    SkipModelValidation?: SkipModelValidation;
    /**
     * The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.
     */
    SourceUri?: ModelPackageSourceUri;
    /**
     * The KMS Key ID (KMSKeyId) used for encryption of model package information.
     */
    SecurityConfig?: ModelPackageSecurityConfig;
    /**
     * The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
     */
    ModelCard?: ModelPackageModelCard;
  }
  export interface CreateModelPackageOutput {
    /**
     * The Amazon Resource Name (ARN) of the new model package.
     */
    ModelPackageArn: ModelPackageArn;
  }
  export interface CreateModelQualityJobDefinitionRequest {
    /**
     * The name of the monitoring job definition.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * Specifies the constraints and baselines for the monitoring job.
     */
    ModelQualityBaselineConfig?: ModelQualityBaselineConfig;
    /**
     * The container that runs the monitoring job.
     */
    ModelQualityAppSpecification: ModelQualityAppSpecification;
    /**
     * A list of the inputs that are monitored. Currently endpoints are supported.
     */
    ModelQualityJobInput: ModelQualityJobInput;
    ModelQualityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Specifies the network configuration for the monitoring job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
    /**
     * (Optional) An array of key-value pairs. For more information, see  Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateModelQualityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model quality monitoring job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
  }
  export interface CreateMonitoringScheduleRequest {
    /**
     * The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The configuration object that specifies the monitoring schedule and defines the monitoring job.
     */
    MonitoringScheduleConfig: MonitoringScheduleConfig;
    /**
     * (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateMonitoringScheduleResponse {
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn: MonitoringScheduleArn;
  }
  export interface CreateNotebookInstanceInput {
    /**
     * The name of the new notebook instance.
     */
    NotebookInstanceName: NotebookInstanceName;
    /**
     * The type of ML compute instance to launch for the notebook instance.
     */
    InstanceType: InstanceType;
    /**
     * The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance. 
     */
    SubnetId?: SubnetId;
    /**
     * The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet. 
     */
    SecurityGroupIds?: SecurityGroupIds;
    /**
     *  When you send any requests to Amazon Web Services resources from the notebook instance, SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so SageMaker can perform these tasks. The policy must allow the SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see SageMaker Roles.   To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission. 
     */
    RoleArn: RoleArn;
    /**
     * The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service key that SageMaker uses to encrypt data on the storage volume attached to your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the Amazon Web Services Key Management Service Developer Guide.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
     */
    LifecycleConfigName?: NotebookInstanceLifecycleConfigName;
    /**
     * Sets whether SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance is able to access resources only in your VPC, and is not be able to connect to SageMaker training and endpoint services unless you configure a NAT Gateway in your VPC. For more information, see Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.
     */
    DirectInternetAccess?: DirectInternetAccess;
    /**
     * The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.
     */
    VolumeSizeInGB?: NotebookInstanceVolumeSizeInGB;
    /**
     * A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
     */
    AcceleratorTypes?: NotebookInstanceAcceleratorTypes;
    /**
     * A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    DefaultCodeRepository?: CodeRepositoryNameOrUrl;
    /**
     * An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    AdditionalCodeRepositories?: AdditionalCodeRepositoryNamesOrUrls;
    /**
     * Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.  Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users. 
     */
    RootAccess?: RootAccess;
    /**
     * The platform identifier of the notebook instance runtime environment.
     */
    PlatformIdentifier?: PlatformIdentifier;
    /**
     * Information on the IMDS configuration of the notebook instance
     */
    InstanceMetadataServiceConfiguration?: InstanceMetadataServiceConfiguration;
  }
  export interface CreateNotebookInstanceLifecycleConfigInput {
    /**
     * The name of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName;
    /**
     * A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
     */
    OnCreate?: NotebookInstanceLifecycleConfigList;
    /**
     * A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
     */
    OnStart?: NotebookInstanceLifecycleConfigList;
  }
  export interface CreateNotebookInstanceLifecycleConfigOutput {
    /**
     * The Amazon Resource Name (ARN) of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigArn?: NotebookInstanceLifecycleConfigArn;
  }
  export interface CreateNotebookInstanceOutput {
    /**
     * The Amazon Resource Name (ARN) of the notebook instance. 
     */
    NotebookInstanceArn?: NotebookInstanceArn;
  }
  export interface CreateOptimizationJobRequest {
    /**
     * A custom name for the new optimization job.
     */
    OptimizationJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.  During model optimization, Amazon SageMaker needs your permission to:   Read input data from an S3 bucket   Write model artifacts to an S3 bucket   Write logs to Amazon CloudWatch Logs   Publish metrics to Amazon CloudWatch   You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles. 
     */
    RoleArn: RoleArn;
    /**
     * The location of the source model to optimize with an optimization job.
     */
    ModelSource: OptimizationJobModelSource;
    /**
     * The type of instance that hosts the optimized model that you create with the optimization job.
     */
    DeploymentInstanceType: OptimizationJobDeploymentInstanceType;
    /**
     * The environment variables to set in the model container.
     */
    OptimizationEnvironment?: OptimizationJobEnvironmentVariables;
    /**
     * Settings for each of the optimization techniques that the job applies.
     */
    OptimizationConfigs: OptimizationConfigs;
    /**
     * Details for where to store the optimized model that you create with the optimization job.
     */
    OutputConfig: OptimizationJobOutputConfig;
    StoppingCondition: StoppingCondition;
    /**
     * A list of key-value pairs associated with the optimization job. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
    /**
     * A VPC in Amazon VPC that your optimized model has access to.
     */
    VpcConfig?: OptimizationVpcConfig;
  }
  export interface CreateOptimizationJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the optimization job.
     */
    OptimizationJobArn: OptimizationJobArn;
  }
  export interface CreatePipelineRequest {
    /**
     * The name of the pipeline.
     */
    PipelineName: PipelineName;
    /**
     * The display name of the pipeline.
     */
    PipelineDisplayName?: PipelineName;
    /**
     * The JSON pipeline definition of the pipeline.
     */
    PipelineDefinition?: PipelineDefinition;
    /**
     * The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
     */
    PipelineDefinitionS3Location?: PipelineDefinitionS3Location;
    /**
     * A description of the pipeline.
     */
    PipelineDescription?: PipelineDescription;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
     */
    ClientRequestToken: IdempotencyToken;
    /**
     * The Amazon Resource Name (ARN) of the role used by the pipeline to access and create resources.
     */
    RoleArn: RoleArn;
    /**
     * A list of tags to apply to the created pipeline.
     */
    Tags?: TagList;
    /**
     * This is the configuration that controls the parallelism of the pipeline. If specified, it applies to all runs of this pipeline by default.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
  }
  export interface CreatePipelineResponse {
    /**
     * The Amazon Resource Name (ARN) of the created pipeline.
     */
    PipelineArn?: PipelineArn;
  }
  export interface CreatePresignedDomainUrlRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The name of the UserProfile to sign-in as.
     */
    UserProfileName: UserProfileName;
    /**
     * The session expiration duration in seconds. This value defaults to 43200.
     */
    SessionExpirationDurationInSeconds?: SessionExpirationDurationInSeconds;
    /**
     * The number of seconds until the pre-signed URL expires. This value defaults to 300.
     */
    ExpiresInSeconds?: ExpiresInSeconds;
    /**
     * The name of the space.
     */
    SpaceName?: SpaceName;
    /**
     * The landing page that the user is directed to when accessing the presigned URL. Using this value, users can access Studio or Studio Classic, even if it is not the default experience for the domain. The supported values are:    studio::relative/path: Directs users to the relative path in Studio.    app:JupyterServer:relative/path: Directs users to the relative path in the Studio Classic application.    app:JupyterLab:relative/path: Directs users to the relative path in the JupyterLab application.    app:RStudioServerPro:relative/path: Directs users to the relative path in the RStudio application.    app:CodeEditor:relative/path: Directs users to the relative path in the Code Editor, based on Code-OSS, Visual Studio Code - Open Source application.    app:Canvas:relative/path: Directs users to the relative path in the Canvas application.  
     */
    LandingUri?: LandingUri;
  }
  export interface CreatePresignedDomainUrlResponse {
    /**
     * The presigned URL.
     */
    AuthorizedUrl?: PresignedDomainUrl;
  }
  export interface CreatePresignedMlflowTrackingServerUrlRequest {
    /**
     * The name of the tracking server to connect to your MLflow UI.
     */
    TrackingServerName: TrackingServerName;
    /**
     * The duration in seconds that your presigned URL is valid. The presigned URL can be used only once.
     */
    ExpiresInSeconds?: ExpiresInSeconds;
    /**
     * The duration in seconds that your MLflow UI session is valid.
     */
    SessionExpirationDurationInSeconds?: SessionExpirationDurationInSeconds;
  }
  export interface CreatePresignedMlflowTrackingServerUrlResponse {
    /**
     * A presigned URL with an authorization token.
     */
    AuthorizedUrl?: TrackingServerUrl;
  }
  export interface CreatePresignedNotebookInstanceUrlInput {
    /**
     * The name of the notebook instance.
     */
    NotebookInstanceName: NotebookInstanceName;
    /**
     * The duration of the session, in seconds. The default is 12 hours.
     */
    SessionExpirationDurationInSeconds?: SessionExpirationDurationInSeconds;
  }
  export interface CreatePresignedNotebookInstanceUrlOutput {
    /**
     * A JSON object that contains the URL string. 
     */
    AuthorizedUrl?: NotebookInstanceUrl;
  }
  export interface CreateProcessingJobRequest {
    /**
     * An array of inputs configuring the data to download into the processing container.
     */
    ProcessingInputs?: ProcessingInputs;
    /**
     * Output configuration for the processing job.
     */
    ProcessingOutputConfig?: ProcessingOutputConfig;
    /**
     *  The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    ProcessingJobName: ProcessingJobName;
    /**
     * Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
     */
    ProcessingResources: ProcessingResources;
    /**
     * The time limit for how long the processing job is allowed to run.
     */
    StoppingCondition?: ProcessingStoppingCondition;
    /**
     * Configures the processing job to run a specified Docker container image.
     */
    AppSpecification: AppSpecification;
    /**
     * The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.
     */
    Environment?: ProcessingEnvironmentMap;
    /**
     * Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.
     */
    NetworkConfig?: NetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    /**
     * (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
    ExperimentConfig?: ExperimentConfig;
  }
  export interface CreateProcessingJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the processing job.
     */
    ProcessingJobArn: ProcessingJobArn;
  }
  export interface CreateProjectInput {
    /**
     * The name of the project.
     */
    ProjectName: ProjectEntityName;
    /**
     * A description for the project.
     */
    ProjectDescription?: EntityDescription;
    /**
     * The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog.
     */
    ServiceCatalogProvisioningDetails: ServiceCatalogProvisioningDetails;
    /**
     * An array of key-value pairs that you want to use to organize and track your Amazon Web Services resource costs. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
  }
  export interface CreateProjectOutput {
    /**
     * The Amazon Resource Name (ARN) of the project.
     */
    ProjectArn: ProjectArn;
    /**
     * The ID of the new project.
     */
    ProjectId: ProjectId;
  }
  export interface CreateSpaceRequest {
    /**
     * The ID of the associated domain.
     */
    DomainId: DomainId;
    /**
     * The name of the space.
     */
    SpaceName: SpaceName;
    /**
     * Tags to associated with the space. Each tag consists of a key and an optional value. Tag keys must be unique for each resource. Tags are searchable using the Search API.
     */
    Tags?: TagList;
    /**
     * A collection of space settings.
     */
    SpaceSettings?: SpaceSettings;
    /**
     * A collection of ownership settings.
     */
    OwnershipSettings?: OwnershipSettings;
    /**
     * A collection of space sharing settings.
     */
    SpaceSharingSettings?: SpaceSharingSettings;
    /**
     * The name of the space that appears in the SageMaker Studio UI.
     */
    SpaceDisplayName?: NonEmptyString64;
  }
  export interface CreateSpaceResponse {
    /**
     * The space's Amazon Resource Name (ARN).
     */
    SpaceArn?: SpaceArn;
  }
  export interface CreateStudioLifecycleConfigRequest {
    /**
     * The name of the Amazon SageMaker Studio Lifecycle Configuration to create.
     */
    StudioLifecycleConfigName: StudioLifecycleConfigName;
    /**
     * The content of your Amazon SageMaker Studio Lifecycle Configuration script. This content must be base64 encoded.
     */
    StudioLifecycleConfigContent: StudioLifecycleConfigContent;
    /**
     * The App type that the Lifecycle Configuration is attached to.
     */
    StudioLifecycleConfigAppType: StudioLifecycleConfigAppType;
    /**
     * Tags to be associated with the Lifecycle Configuration. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API. 
     */
    Tags?: TagList;
  }
  export interface CreateStudioLifecycleConfigResponse {
    /**
     * The ARN of your created Lifecycle Configuration.
     */
    StudioLifecycleConfigArn?: StudioLifecycleConfigArn;
  }
  export interface CreateTrainingJobRequest {
    /**
     * The name of the training job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. 
     */
    TrainingJobName: TrainingJobName;
    /**
     * Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.  You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.   Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error. 
     */
    HyperParameters?: HyperParameters;
    /**
     * The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by SageMaker, see Algorithms. For information about providing your own algorithms, see Using Your Own Algorithms with Amazon SageMaker. 
     */
    AlgorithmSpecification: AlgorithmSpecification;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that SageMaker can assume to perform tasks on your behalf.  During model training, SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see SageMaker Roles.   To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission. 
     */
    RoleArn: RoleArn;
    /**
     * An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.  Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3, EFS, or FSx location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.  Depending on the input mode that the algorithm supports, SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams. For example, if you specify an EFS location, input data files are available as input streams. They do not need to be downloaded. Your input must be in the same Amazon Web Services region as your training job.
     */
    InputDataConfig?: InputDataConfig;
    /**
     * Specifies the path to the S3 location where you want to store model artifacts. SageMaker creates subfolders for the artifacts. 
     */
    OutputDataConfig: OutputDataConfig;
    /**
     * The resources, including the ML compute instances and ML storage volumes, to use for model training.  ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.
     */
    ResourceConfig: ResourceConfig;
    /**
     * A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: VpcConfig;
    /**
     * Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. 
     */
    StoppingCondition: StoppingCondition;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.  The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed. 
     */
    EnableManagedSpotTraining?: Boolean;
    /**
     * Contains information about the output location for managed spot training checkpoint data.
     */
    CheckpointConfig?: CheckpointConfig;
    DebugHookConfig?: DebugHookConfig;
    /**
     * Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
     */
    DebugRuleConfigurations?: DebugRuleConfigurations;
    TensorBoardOutputConfig?: TensorBoardOutputConfig;
    ExperimentConfig?: ExperimentConfig;
    ProfilerConfig?: ProfilerConfig;
    /**
     * Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
     */
    ProfilerRuleConfigurations?: ProfilerRuleConfigurations;
    /**
     * The environment variables to set in the Docker container.
     */
    Environment?: TrainingEnvironmentMap;
    /**
     * The number of times to retry the job when the job fails due to an InternalServerError.
     */
    RetryStrategy?: RetryStrategy;
    /**
     * Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
     */
    RemoteDebugConfig?: RemoteDebugConfig;
    /**
     * Contains information about the infrastructure health check configuration for the training job.
     */
    InfraCheckConfig?: InfraCheckConfig;
    /**
     * Contains information about attribute-based access control (ABAC) for the training job.
     */
    SessionChainingConfig?: SessionChainingConfig;
  }
  export interface CreateTrainingJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn: TrainingJobArn;
  }
  export interface CreateTransformJobRequest {
    /**
     * The name of the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. 
     */
    TransformJobName: TransformJobName;
    /**
     * The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an Amazon Web Services Region in an Amazon Web Services account.
     */
    ModelName: ModelName;
    /**
     * The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
     */
    MaxConcurrentTransforms?: MaxConcurrentTransforms;
    /**
     * Configures the timeout and maximum number of retries for processing a transform job invocation.
     */
    ModelClientConfig?: ModelClientConfig;
    /**
     * The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.  The value of MaxPayloadInMB cannot be greater than 100 MB. If you specify the MaxConcurrentTransforms parameter, the value of (MaxConcurrentTransforms * MaxPayloadInMB) also cannot exceed 100 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.
     */
    MaxPayloadInMB?: MaxPayloadInMB;
    /**
     * Specifies the number of records to include in a mini-batch for an HTTP inference request. A record  is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.  To enable the batch strategy, you must set the SplitType property to Line, RecordIO, or TFRecord. To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line. To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.
     */
    BatchStrategy?: BatchStrategy;
    /**
     * The environment variables to set in the Docker container. Don't include any sensitive data in your environment variables. We support up to 16 key and values entries in the map.
     */
    Environment?: TransformEnvironmentMap;
    /**
     * Describes the input source and the way the transform job consumes it.
     */
    TransformInput: TransformInput;
    /**
     * Describes the results of the transform job.
     */
    TransformOutput: TransformOutput;
    /**
     * Configuration to control how SageMaker captures inference data.
     */
    DataCaptureConfig?: BatchDataCaptureConfig;
    /**
     * Describes the resources, including ML instance types and ML instance count, to use for the transform job.
     */
    TransformResources: TransformResources;
    /**
     * The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.
     */
    DataProcessing?: DataProcessing;
    /**
     * (Optional) An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
    ExperimentConfig?: ExperimentConfig;
  }
  export interface CreateTransformJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the transform job.
     */
    TransformJobArn: TransformJobArn;
  }
  export interface CreateTrialComponentRequest {
    /**
     * The name of the component. The name must be unique in your Amazon Web Services account and is not case-sensitive.
     */
    TrialComponentName: ExperimentEntityName;
    /**
     * The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The status of the component. States include:   InProgress   Completed   Failed  
     */
    Status?: TrialComponentStatus;
    /**
     * When the component started.
     */
    StartTime?: Timestamp;
    /**
     * When the component ended.
     */
    EndTime?: Timestamp;
    /**
     * The hyperparameters for the component.
     */
    Parameters?: TrialComponentParameters;
    /**
     * The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.
     */
    InputArtifacts?: TrialComponentArtifacts;
    /**
     * The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.
     */
    OutputArtifacts?: TrialComponentArtifacts;
    MetadataProperties?: MetadataProperties;
    /**
     * A list of tags to associate with the component. You can use Search API to search on the tags.
     */
    Tags?: TagList;
  }
  export interface CreateTrialComponentResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
  }
  export interface CreateTrialRequest {
    /**
     * The name of the trial. The name must be unique in your Amazon Web Services account and is not case-sensitive.
     */
    TrialName: ExperimentEntityName;
    /**
     * The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The name of the experiment to associate the trial with.
     */
    ExperimentName: ExperimentEntityName;
    MetadataProperties?: MetadataProperties;
    /**
     * A list of tags to associate with the trial. You can use Search API to search on the tags.
     */
    Tags?: TagList;
  }
  export interface CreateTrialResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
  }
  export interface CreateUserProfileRequest {
    /**
     * The ID of the associated Domain.
     */
    DomainId: DomainId;
    /**
     * A name for the UserProfile. This value is not case sensitive.
     */
    UserProfileName: UserProfileName;
    /**
     * A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is IAM Identity Center, this field is required. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified. 
     */
    SingleSignOnUserIdentifier?: SingleSignOnUserIdentifier;
    /**
     * The username of the associated Amazon Web Services Single Sign-On User for this UserProfile. If the Domain's AuthMode is IAM Identity Center, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not IAM Identity Center, this field cannot be specified. 
     */
    SingleSignOnUserValue?: String256;
    /**
     * Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags that you specify for the User Profile are also added to all Apps that the User Profile launches.
     */
    Tags?: TagList;
    /**
     * A collection of settings.
     */
    UserSettings?: UserSettings;
  }
  export interface CreateUserProfileResponse {
    /**
     * The user profile Amazon Resource Name (ARN).
     */
    UserProfileArn?: UserProfileArn;
  }
  export interface CreateWorkforceRequest {
    /**
     * Use this parameter to configure an Amazon Cognito private workforce. A single Cognito workforce is created using and corresponds to a single  Amazon Cognito user pool. Do not use OidcConfig if you specify values for CognitoConfig.
     */
    CognitoConfig?: CognitoConfig;
    /**
     * Use this parameter to configure a private workforce using your own OIDC Identity Provider. Do not use CognitoConfig if you specify values for OidcConfig.
     */
    OidcConfig?: OidcConfig;
    SourceIpConfig?: SourceIpConfig;
    /**
     * The name of the private workforce.
     */
    WorkforceName: WorkforceName;
    /**
     * An array of key-value pairs that contain metadata to help you categorize and organize our workforce. Each tag consists of a key and a value, both of which you define.
     */
    Tags?: TagList;
    /**
     * Use this parameter to configure a workforce using VPC.
     */
    WorkforceVpcConfig?: WorkforceVpcConfigRequest;
  }
  export interface CreateWorkforceResponse {
    /**
     * The Amazon Resource Name (ARN) of the workforce.
     */
    WorkforceArn: WorkforceArn;
  }
  export interface CreateWorkteamRequest {
    /**
     * The name of the work team. Use this name to identify the work team.
     */
    WorkteamName: WorkteamName;
    /**
     * The name of the workforce.
     */
    WorkforceName?: WorkforceName;
    /**
     * A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.  Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. Do not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups.
     */
    MemberDefinitions: MemberDefinitions;
    /**
     * A description of the work team.
     */
    Description: String200;
    /**
     * Configures notification of workers regarding available or expiring work items.
     */
    NotificationConfiguration?: NotificationConfiguration;
    /**
     * Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
     */
    WorkerAccessConfiguration?: WorkerAccessConfiguration;
    /**
     * An array of key-value pairs. For more information, see Resource Tag and Using Cost Allocation Tags in the  Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export interface CreateWorkteamResponse {
    /**
     * The Amazon Resource Name (ARN) of the work team. You can use this ARN to identify the work team.
     */
    WorkteamArn?: WorkteamArn;
  }
  export type CreationTime = Date;
  export type CrossAccountFilterOption = "SameAccount"|"CrossAccount"|string;
  export type CsvContentType = string;
  export type CsvContentTypes = CsvContentType[];
  export interface CustomFileSystem {
    /**
     * A custom file system in Amazon EFS.
     */
    EFSFileSystem?: EFSFileSystem;
  }
  export interface CustomFileSystemConfig {
    /**
     * The settings for a custom Amazon EFS file system.
     */
    EFSFileSystemConfig?: EFSFileSystemConfig;
  }
  export type CustomFileSystemConfigs = CustomFileSystemConfig[];
  export type CustomFileSystems = CustomFileSystem[];
  export interface CustomImage {
    /**
     * The name of the CustomImage. Must be unique to your account.
     */
    ImageName: ImageName;
    /**
     * The version number of the CustomImage.
     */
    ImageVersionNumber?: ImageVersionNumber;
    /**
     * The name of the AppImageConfig.
     */
    AppImageConfigName: AppImageConfigName;
  }
  export type CustomImageContainerArguments = NonEmptyString64[];
  export type CustomImageContainerEntrypoint = NonEmptyString256[];
  export type CustomImageContainerEnvironmentVariables = {[key: string]: String256};
  export type CustomImages = CustomImage[];
  export interface CustomPosixUserConfig {
    /**
     * The POSIX user ID.
     */
    Uid: Uid;
    /**
     * The POSIX group ID.
     */
    Gid: Gid;
  }
  export type CustomerMetadataKey = string;
  export type CustomerMetadataKeyList = CustomerMetadataKey[];
  export type CustomerMetadataMap = {[key: string]: CustomerMetadataValue};
  export type CustomerMetadataValue = string;
  export interface CustomizedMetricSpecification {
    /**
     * The name of the customized metric.
     */
    MetricName?: String;
    /**
     * The namespace of the customized metric.
     */
    Namespace?: String;
    /**
     * The statistic of the customized metric.
     */
    Statistic?: Statistic;
  }
  export interface DataCaptureConfig {
    /**
     * Whether data capture should be enabled or disabled (defaults to enabled).
     */
    EnableCapture?: EnableCapture;
    /**
     * The percentage of requests SageMaker will capture. A lower value is recommended for Endpoints with high traffic.
     */
    InitialSamplingPercentage: SamplingPercentage;
    /**
     * The Amazon S3 location used to capture the data.
     */
    DestinationS3Uri: DestinationS3Uri;
    /**
     * The Amazon Resource Name (ARN) of an Key Management Service key that SageMaker uses to encrypt the captured data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias   
     */
    KmsKeyId?: KmsKeyId;
    /**
     * Specifies data Model Monitor will capture. You can configure whether to collect only input, only output, or both
     */
    CaptureOptions: CaptureOptionList;
    /**
     * Configuration specifying how to treat different headers. If no headers are specified SageMaker will by default base64 encode when capturing the data.
     */
    CaptureContentTypeHeader?: CaptureContentTypeHeader;
  }
  export interface DataCaptureConfigSummary {
    /**
     * Whether data capture is enabled or disabled.
     */
    EnableCapture: EnableCapture;
    /**
     * Whether data capture is currently functional.
     */
    CaptureStatus: CaptureStatus;
    /**
     * The percentage of requests being captured by your Endpoint.
     */
    CurrentSamplingPercentage: SamplingPercentage;
    /**
     * The Amazon S3 location being used to capture the data.
     */
    DestinationS3Uri: DestinationS3Uri;
    /**
     * The KMS key being used to encrypt the data in Amazon S3.
     */
    KmsKeyId: KmsKeyId;
  }
  export interface DataCatalogConfig {
    /**
     * The name of the Glue table.
     */
    TableName: TableName;
    /**
     * The name of the Glue table catalog.
     */
    Catalog: Catalog;
    /**
     * The name of the Glue table database.
     */
    Database: Database;
  }
  export type DataDistributionType = "FullyReplicated"|"ShardedByS3Key"|string;
  export type DataExplorationNotebookLocation = string;
  export type DataInputConfig = string;
  export interface DataProcessing {
    /**
     * A JSONPath expression used to select a portion of the input data to pass to the algorithm. Use the InputFilter parameter to exclude fields, such as an ID column, from the input. If you want SageMaker to pass the entire input dataset to the algorithm, accept the default value $. Examples: "$", "$[1:]", "$.features" 
     */
    InputFilter?: JsonPath;
    /**
     * A JSONPath expression used to select a portion of the joined dataset to save in the output file for a batch transform job. If you want SageMaker to store the entire input dataset in the output file, leave the default value, $. If you specify indexes that aren't within the dimension size of the joined dataset, you get an error. Examples: "$", "$[0,5:]", "$['id','SageMakerOutput']" 
     */
    OutputFilter?: JsonPath;
    /**
     * Specifies the source of the data to join with the transformed data. The valid values are None and Input. The default value is None, which specifies not to join the input with the transformed data. If you want the batch transform job to join the original input data with the transformed data, set JoinSource to Input. You can specify OutputFilter as an additional filter to select a portion of the joined dataset and store it in the output file. For JSON or JSONLines objects, such as a JSON array, SageMaker adds the transformed data to the input JSON object in an attribute called SageMakerOutput. The joined result for JSON must be a key-value pair object. If the input is not a key-value pair object, SageMaker creates a new JSON file. In the new JSON file, and the input data is stored under the SageMakerInput key and the results are stored in SageMakerOutput. For CSV data, SageMaker takes each row as a JSON array and joins the transformed data with the input by appending each transformed row to the end of the input. The joined data has the original input data followed by the transformed data and the output is a CSV file. For information on how joining in applied, see Workflow for Associating Inferences with Input Records.
     */
    JoinSource?: JoinSource;
  }
  export interface DataQualityAppSpecification {
    /**
     * The container image that the data quality monitoring job runs.
     */
    ImageUri: ImageUri;
    /**
     * The entrypoint for a container used to run a monitoring job.
     */
    ContainerEntrypoint?: ContainerEntrypoint;
    /**
     * The arguments to send to the container that the monitoring job runs.
     */
    ContainerArguments?: MonitoringContainerArguments;
    /**
     * An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
     */
    RecordPreprocessorSourceUri?: S3Uri;
    /**
     * An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
     */
    PostAnalyticsProcessorSourceUri?: S3Uri;
    /**
     * Sets the environment variables in the container that the monitoring job runs.
     */
    Environment?: MonitoringEnvironmentMap;
  }
  export interface DataQualityBaselineConfig {
    /**
     * The name of the job that performs baselining for the data quality monitoring job.
     */
    BaseliningJobName?: ProcessingJobName;
    ConstraintsResource?: MonitoringConstraintsResource;
    StatisticsResource?: MonitoringStatisticsResource;
  }
  export interface DataQualityJobInput {
    EndpointInput?: EndpointInput;
    /**
     * Input object for the batch transform job.
     */
    BatchTransformInput?: BatchTransformInput;
  }
  export interface DataSource {
    /**
     * The S3 location of the data source that is associated with a channel.
     */
    S3DataSource?: S3DataSource;
    /**
     * The file system that is associated with a channel.
     */
    FileSystemDataSource?: FileSystemDataSource;
  }
  export type DataSourceName = "SalesforceGenie"|"Snowflake"|string;
  export type Database = string;
  export interface DatasetDefinition {
    AthenaDatasetDefinition?: AthenaDatasetDefinition;
    RedshiftDatasetDefinition?: RedshiftDatasetDefinition;
    /**
     * The local path where you want Amazon SageMaker to download the Dataset Definition inputs to run a processing job. LocalPath is an absolute path to the input data. This is a required parameter when AppManaged is False (default).
     */
    LocalPath?: ProcessingLocalPath;
    /**
     * Whether the generated dataset is FullyReplicated or ShardedByS3Key (default).
     */
    DataDistributionType?: DataDistributionType;
    /**
     * Whether to use File or Pipe input mode. In File (default) mode, Amazon SageMaker copies the data from the input source onto the local Amazon Elastic Block Store (Amazon EBS) volumes before starting your training algorithm. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your algorithm without using the EBS volume.
     */
    InputMode?: InputMode;
  }
  export interface DebugHookConfig {
    /**
     * Path to local storage location for metrics and tensors. Defaults to /opt/ml/output/tensors/.
     */
    LocalPath?: DirectoryPath;
    /**
     * Path to Amazon S3 storage location for metrics and tensors.
     */
    S3OutputPath: S3Uri;
    /**
     * Configuration information for the Amazon SageMaker Debugger hook parameters.
     */
    HookParameters?: HookParameters;
    /**
     * Configuration information for Amazon SageMaker Debugger tensor collections. To learn more about how to configure the CollectionConfiguration parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. 
     */
    CollectionConfigurations?: CollectionConfigurations;
  }
  export interface DebugRuleConfiguration {
    /**
     * The name of the rule configuration. It must be unique relative to other rule configuration names.
     */
    RuleConfigurationName: RuleConfigurationName;
    /**
     * Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/.
     */
    LocalPath?: DirectoryPath;
    /**
     * Path to Amazon S3 storage location for rules.
     */
    S3OutputPath?: S3Uri;
    /**
     * The Amazon Elastic Container (ECR) Image for the managed rule evaluation.
     */
    RuleEvaluatorImage: AlgorithmImage;
    /**
     * The instance type to deploy a custom rule for debugging a training job.
     */
    InstanceType?: ProcessingInstanceType;
    /**
     * The size, in GB, of the ML storage volume attached to the processing instance.
     */
    VolumeSizeInGB?: OptionalVolumeSizeInGB;
    /**
     * Runtime configuration for rule container.
     */
    RuleParameters?: RuleParameters;
  }
  export type DebugRuleConfigurations = DebugRuleConfiguration[];
  export interface DebugRuleEvaluationStatus {
    /**
     * The name of the rule configuration.
     */
    RuleConfigurationName?: RuleConfigurationName;
    /**
     * The Amazon Resource Name (ARN) of the rule evaluation job.
     */
    RuleEvaluationJobArn?: ProcessingJobArn;
    /**
     * Status of the rule evaluation.
     */
    RuleEvaluationStatus?: RuleEvaluationStatus;
    /**
     * Details from the rule evaluation.
     */
    StatusDetails?: StatusDetails;
    /**
     * Timestamp when the rule evaluation status was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type DebugRuleEvaluationStatuses = DebugRuleEvaluationStatus[];
  export interface DefaultEbsStorageSettings {
    /**
     * The default size of the EBS storage volume for a space.
     */
    DefaultEbsVolumeSizeInGb: SpaceEbsVolumeSizeInGb;
    /**
     * The maximum size of the EBS storage volume for a space.
     */
    MaximumEbsVolumeSizeInGb: SpaceEbsVolumeSizeInGb;
  }
  export type DefaultGid = number;
  export interface DefaultSpaceSettings {
    /**
     * The ARN of the execution role for the space.
     */
    ExecutionRole?: RoleArn;
    /**
     * The security group IDs for the Amazon VPC that the space uses for communication.
     */
    SecurityGroups?: SecurityGroupIds;
    JupyterServerAppSettings?: JupyterServerAppSettings;
    KernelGatewayAppSettings?: KernelGatewayAppSettings;
    JupyterLabAppSettings?: JupyterLabAppSettings;
    SpaceStorageSettings?: DefaultSpaceStorageSettings;
    CustomPosixUserConfig?: CustomPosixUserConfig;
    /**
     * The settings for assigning a custom file system to a domain. Permitted users can access this file system in Amazon SageMaker Studio.
     */
    CustomFileSystemConfigs?: CustomFileSystemConfigs;
  }
  export interface DefaultSpaceStorageSettings {
    /**
     * The default EBS storage settings for a space.
     */
    DefaultEbsStorageSettings?: DefaultEbsStorageSettings;
  }
  export type DefaultUid = number;
  export interface DeleteActionRequest {
    /**
     * The name of the action to delete.
     */
    ActionName: ExperimentEntityName;
  }
  export interface DeleteActionResponse {
    /**
     * The Amazon Resource Name (ARN) of the action.
     */
    ActionArn?: ActionArn;
  }
  export interface DeleteAlgorithmInput {
    /**
     * The name of the algorithm to delete.
     */
    AlgorithmName: EntityName;
  }
  export interface DeleteAppImageConfigRequest {
    /**
     * The name of the AppImageConfig to delete.
     */
    AppImageConfigName: AppImageConfigName;
  }
  export interface DeleteAppRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name. If this value is not set, then SpaceName must be set.
     */
    UserProfileName?: UserProfileName;
    /**
     * The name of the space. If this value is not set, then UserProfileName must be set.
     */
    SpaceName?: SpaceName;
    /**
     * The type of app.
     */
    AppType: AppType;
    /**
     * The name of the app.
     */
    AppName: AppName;
  }
  export interface DeleteArtifactRequest {
    /**
     * The Amazon Resource Name (ARN) of the artifact to delete.
     */
    ArtifactArn?: ArtifactArn;
    /**
     * The URI of the source.
     */
    Source?: ArtifactSource;
  }
  export interface DeleteArtifactResponse {
    /**
     * The Amazon Resource Name (ARN) of the artifact.
     */
    ArtifactArn?: ArtifactArn;
  }
  export interface DeleteAssociationRequest {
    /**
     * The ARN of the source.
     */
    SourceArn: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination.
     */
    DestinationArn: AssociationEntityArn;
  }
  export interface DeleteAssociationResponse {
    /**
     * The ARN of the source.
     */
    SourceArn?: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination.
     */
    DestinationArn?: AssociationEntityArn;
  }
  export interface DeleteClusterRequest {
    /**
     * The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
     */
    ClusterName: ClusterNameOrArn;
  }
  export interface DeleteClusterResponse {
    /**
     * The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster to delete.
     */
    ClusterArn: ClusterArn;
  }
  export interface DeleteCodeRepositoryInput {
    /**
     * The name of the Git repository to delete.
     */
    CodeRepositoryName: EntityName;
  }
  export interface DeleteCompilationJobRequest {
    /**
     * The name of the compilation job to delete.
     */
    CompilationJobName: EntityName;
  }
  export interface DeleteContextRequest {
    /**
     * The name of the context to delete.
     */
    ContextName: ContextName;
  }
  export interface DeleteContextResponse {
    /**
     * The Amazon Resource Name (ARN) of the context.
     */
    ContextArn?: ContextArn;
  }
  export interface DeleteDataQualityJobDefinitionRequest {
    /**
     * The name of the data quality monitoring job definition to delete.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DeleteDeviceFleetRequest {
    /**
     * The name of the fleet to delete.
     */
    DeviceFleetName: EntityName;
  }
  export interface DeleteDomainRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted). 
     */
    RetentionPolicy?: RetentionPolicy;
  }
  export interface DeleteEdgeDeploymentPlanRequest {
    /**
     * The name of the edge deployment plan to delete.
     */
    EdgeDeploymentPlanName: EntityName;
  }
  export interface DeleteEdgeDeploymentStageRequest {
    /**
     * The name of the edge deployment plan from which the stage will be deleted.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * The name of the stage.
     */
    StageName: EntityName;
  }
  export interface DeleteEndpointConfigInput {
    /**
     * The name of the endpoint configuration that you want to delete.
     */
    EndpointConfigName: EndpointConfigName;
  }
  export interface DeleteEndpointInput {
    /**
     * The name of the endpoint that you want to delete.
     */
    EndpointName: EndpointName;
  }
  export interface DeleteExperimentRequest {
    /**
     * The name of the experiment to delete.
     */
    ExperimentName: ExperimentEntityName;
  }
  export interface DeleteExperimentResponse {
    /**
     * The Amazon Resource Name (ARN) of the experiment that is being deleted.
     */
    ExperimentArn?: ExperimentArn;
  }
  export interface DeleteFeatureGroupRequest {
    /**
     * The name of the FeatureGroup you want to delete. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account. 
     */
    FeatureGroupName: FeatureGroupName;
  }
  export interface DeleteFlowDefinitionRequest {
    /**
     * The name of the flow definition you are deleting.
     */
    FlowDefinitionName: FlowDefinitionName;
  }
  export interface DeleteFlowDefinitionResponse {
  }
  export interface DeleteHubContentReferenceRequest {
    /**
     * The name of the hub to delete the hub content reference from.
     */
    HubName: HubNameOrArn;
    /**
     * The type of hub content reference to delete. The only supported type of hub content reference to delete is ModelReference.
     */
    HubContentType: HubContentType;
    /**
     * The name of the hub content to delete.
     */
    HubContentName: HubContentName;
  }
  export interface DeleteHubContentRequest {
    /**
     * The name of the hub that you want to delete content in.
     */
    HubName: HubNameOrArn;
    /**
     * The type of content that you want to delete from a hub.
     */
    HubContentType: HubContentType;
    /**
     * The name of the content that you want to delete from a hub.
     */
    HubContentName: HubContentName;
    /**
     * The version of the content that you want to delete from a hub.
     */
    HubContentVersion: HubContentVersion;
  }
  export interface DeleteHubRequest {
    /**
     * The name of the hub to delete.
     */
    HubName: HubNameOrArn;
  }
  export interface DeleteHumanTaskUiRequest {
    /**
     * The name of the human task user interface (work task template) you want to delete.
     */
    HumanTaskUiName: HumanTaskUiName;
  }
  export interface DeleteHumanTaskUiResponse {
  }
  export interface DeleteHyperParameterTuningJobRequest {
    /**
     * The name of the hyperparameter tuning job that you want to delete.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
  }
  export interface DeleteImageRequest {
    /**
     * The name of the image to delete.
     */
    ImageName: ImageName;
  }
  export interface DeleteImageResponse {
  }
  export interface DeleteImageVersionRequest {
    /**
     * The name of the image to delete.
     */
    ImageName: ImageName;
    /**
     * The version to delete.
     */
    Version?: ImageVersionNumber;
    /**
     * The alias of the image to delete.
     */
    Alias?: SageMakerImageVersionAlias;
  }
  export interface DeleteImageVersionResponse {
  }
  export interface DeleteInferenceComponentInput {
    /**
     * The name of the inference component to delete.
     */
    InferenceComponentName: InferenceComponentName;
  }
  export interface DeleteInferenceExperimentRequest {
    /**
     * The name of the inference experiment you want to delete.
     */
    Name: InferenceExperimentName;
  }
  export interface DeleteInferenceExperimentResponse {
    /**
     * The ARN of the deleted inference experiment.
     */
    InferenceExperimentArn: InferenceExperimentArn;
  }
  export interface DeleteMlflowTrackingServerRequest {
    /**
     * The name of the the tracking server to delete.
     */
    TrackingServerName: TrackingServerName;
  }
  export interface DeleteMlflowTrackingServerResponse {
    /**
     * A TrackingServerArn object, the ARN of the tracking server that is deleted if successfully found.
     */
    TrackingServerArn?: TrackingServerArn;
  }
  export interface DeleteModelBiasJobDefinitionRequest {
    /**
     * The name of the model bias job definition to delete.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DeleteModelCardRequest {
    /**
     * The name of the model card to delete.
     */
    ModelCardName: EntityName;
  }
  export interface DeleteModelExplainabilityJobDefinitionRequest {
    /**
     * The name of the model explainability job definition to delete.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DeleteModelInput {
    /**
     * The name of the model to delete.
     */
    ModelName: ModelName;
  }
  export interface DeleteModelPackageGroupInput {
    /**
     * The name of the model group to delete.
     */
    ModelPackageGroupName: ArnOrName;
  }
  export interface DeleteModelPackageGroupPolicyInput {
    /**
     * The name of the model group for which to delete the policy.
     */
    ModelPackageGroupName: EntityName;
  }
  export interface DeleteModelPackageInput {
    /**
     * The name or Amazon Resource Name (ARN) of the model package to delete. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
     */
    ModelPackageName: VersionedArnOrName;
  }
  export interface DeleteModelQualityJobDefinitionRequest {
    /**
     * The name of the model quality monitoring job definition to delete.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DeleteMonitoringScheduleRequest {
    /**
     * The name of the monitoring schedule to delete.
     */
    MonitoringScheduleName: MonitoringScheduleName;
  }
  export interface DeleteNotebookInstanceInput {
    /**
     * The name of the SageMaker notebook instance to delete.
     */
    NotebookInstanceName: NotebookInstanceName;
  }
  export interface DeleteNotebookInstanceLifecycleConfigInput {
    /**
     * The name of the lifecycle configuration to delete.
     */
    NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName;
  }
  export interface DeleteOptimizationJobRequest {
    /**
     * The name that you assigned to the optimization job.
     */
    OptimizationJobName: EntityName;
  }
  export interface DeletePipelineRequest {
    /**
     * The name of the pipeline to delete.
     */
    PipelineName: PipelineName;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
     */
    ClientRequestToken: IdempotencyToken;
  }
  export interface DeletePipelineResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline to delete.
     */
    PipelineArn?: PipelineArn;
  }
  export interface DeleteProjectInput {
    /**
     * The name of the project to delete.
     */
    ProjectName: ProjectEntityName;
  }
  export interface DeleteSpaceRequest {
    /**
     * The ID of the associated domain.
     */
    DomainId: DomainId;
    /**
     * The name of the space.
     */
    SpaceName: SpaceName;
  }
  export interface DeleteStudioLifecycleConfigRequest {
    /**
     * The name of the Amazon SageMaker Studio Lifecycle Configuration to delete.
     */
    StudioLifecycleConfigName: StudioLifecycleConfigName;
  }
  export interface DeleteTagsInput {
    /**
     * The Amazon Resource Name (ARN) of the resource whose tags you want to delete.
     */
    ResourceArn: ResourceArn;
    /**
     * An array or one or more tag keys to delete.
     */
    TagKeys: TagKeyList;
  }
  export interface DeleteTagsOutput {
  }
  export interface DeleteTrialComponentRequest {
    /**
     * The name of the component to delete.
     */
    TrialComponentName: ExperimentEntityName;
  }
  export interface DeleteTrialComponentResponse {
    /**
     * The Amazon Resource Name (ARN) of the component is being deleted.
     */
    TrialComponentArn?: TrialComponentArn;
  }
  export interface DeleteTrialRequest {
    /**
     * The name of the trial to delete.
     */
    TrialName: ExperimentEntityName;
  }
  export interface DeleteTrialResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial that is being deleted.
     */
    TrialArn?: TrialArn;
  }
  export interface DeleteUserProfileRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name.
     */
    UserProfileName: UserProfileName;
  }
  export interface DeleteWorkforceRequest {
    /**
     * The name of the workforce.
     */
    WorkforceName: WorkforceName;
  }
  export interface DeleteWorkforceResponse {
  }
  export interface DeleteWorkteamRequest {
    /**
     * The name of the work team to delete.
     */
    WorkteamName: WorkteamName;
  }
  export interface DeleteWorkteamResponse {
    /**
     * Returns true if the work team was successfully deleted; otherwise, returns false.
     */
    Success: Success;
  }
  export type DependencyCopyPath = string;
  export type DependencyOriginPath = string;
  export interface DeployedImage {
    /**
     * The image path you specified when you created the model.
     */
    SpecifiedImage?: ContainerImage;
    /**
     * The specific digest path of the image hosted in this ProductionVariant.
     */
    ResolvedImage?: ContainerImage;
    /**
     * The date and time when the image path for the model resolved to the ResolvedImage 
     */
    ResolutionTime?: Timestamp;
  }
  export type DeployedImages = DeployedImage[];
  export interface DeploymentConfig {
    /**
     * Update policy for a blue/green deployment. If this update policy is specified, SageMaker creates a new fleet during the deployment while maintaining the old fleet. SageMaker flips traffic to the new fleet according to the specified traffic routing configuration. Only one update policy should be used in the deployment configuration. If no update policy is specified, SageMaker uses a blue/green deployment strategy with all at once traffic shifting by default.
     */
    BlueGreenUpdatePolicy?: BlueGreenUpdatePolicy;
    /**
     * Specifies a rolling deployment strategy for updating a SageMaker endpoint.
     */
    RollingUpdatePolicy?: RollingUpdatePolicy;
    /**
     * Automatic rollback configuration for handling endpoint deployment failures and recovery.
     */
    AutoRollbackConfiguration?: AutoRollbackConfig;
  }
  export interface DeploymentRecommendation {
    /**
     * Status of the deployment recommendation. The status NOT_APPLICABLE means that SageMaker is unable to provide a default recommendation for the model using the information provided. If the deployment status is IN_PROGRESS, retry your API call after a few seconds to get a COMPLETED deployment recommendation.
     */
    RecommendationStatus: RecommendationStatus;
    /**
     * A list of RealTimeInferenceRecommendation items.
     */
    RealTimeInferenceRecommendations?: RealTimeInferenceRecommendations;
  }
  export interface DeploymentStage {
    /**
     * The name of the stage.
     */
    StageName: EntityName;
    /**
     * Configuration of the devices in the stage.
     */
    DeviceSelectionConfig: DeviceSelectionConfig;
    /**
     * Configuration of the deployment details.
     */
    DeploymentConfig?: EdgeDeploymentConfig;
  }
  export type DeploymentStageMaxResults = number;
  export type DeploymentStageStatusSummaries = DeploymentStageStatusSummary[];
  export interface DeploymentStageStatusSummary {
    /**
     * The name of the stage.
     */
    StageName: EntityName;
    /**
     * Configuration of the devices in the stage.
     */
    DeviceSelectionConfig: DeviceSelectionConfig;
    /**
     * Configuration of the deployment details.
     */
    DeploymentConfig: EdgeDeploymentConfig;
    /**
     * General status of the current state.
     */
    DeploymentStatus: EdgeDeploymentStatus;
  }
  export type DeploymentStages = DeploymentStage[];
  export interface DeregisterDevicesRequest {
    /**
     * The name of the fleet the devices belong to.
     */
    DeviceFleetName: EntityName;
    /**
     * The unique IDs of the devices.
     */
    DeviceNames: DeviceNames;
  }
  export interface DerivedInformation {
    /**
     * The data input configuration that SageMaker Neo automatically derived for the model. When SageMaker Neo derives this information, you don't need to specify the data input configuration when you create a compilation job.
     */
    DerivedDataInputConfig?: DataInputConfig;
  }
  export interface DescribeActionRequest {
    /**
     * The name of the action to describe.
     */
    ActionName: ExperimentEntityNameOrArn;
  }
  export interface DescribeActionResponse {
    /**
     * The name of the action.
     */
    ActionName?: ExperimentEntityNameOrArn;
    /**
     * The Amazon Resource Name (ARN) of the action.
     */
    ActionArn?: ActionArn;
    /**
     * The source of the action.
     */
    Source?: ActionSource;
    /**
     * The type of the action.
     */
    ActionType?: String256;
    /**
     * The description of the action.
     */
    Description?: ExperimentDescription;
    /**
     * The status of the action.
     */
    Status?: ActionStatus;
    /**
     * A list of the action's properties.
     */
    Properties?: LineageEntityParameters;
    /**
     * When the action was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * When the action was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    MetadataProperties?: MetadataProperties;
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
  }
  export interface DescribeAlgorithmInput {
    /**
     * The name of the algorithm to describe.
     */
    AlgorithmName: ArnOrName;
  }
  export interface DescribeAlgorithmOutput {
    /**
     * The name of the algorithm being described.
     */
    AlgorithmName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the algorithm.
     */
    AlgorithmArn: AlgorithmArn;
    /**
     * A brief summary about the algorithm.
     */
    AlgorithmDescription?: EntityDescription;
    /**
     * A timestamp specifying when the algorithm was created.
     */
    CreationTime: CreationTime;
    /**
     * Details about training jobs run by this algorithm.
     */
    TrainingSpecification: TrainingSpecification;
    /**
     * Details about inference jobs that the algorithm runs.
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * Details about configurations for one or more training jobs that SageMaker runs to test the algorithm.
     */
    ValidationSpecification?: AlgorithmValidationSpecification;
    /**
     * The current status of the algorithm.
     */
    AlgorithmStatus: AlgorithmStatus;
    /**
     * Details about the current status of the algorithm.
     */
    AlgorithmStatusDetails: AlgorithmStatusDetails;
    /**
     * The product identifier of the algorithm.
     */
    ProductId?: ProductId;
    /**
     * Whether the algorithm is certified to be listed in Amazon Web Services Marketplace.
     */
    CertifyForMarketplace?: CertifyForMarketplace;
  }
  export interface DescribeAppImageConfigRequest {
    /**
     * The name of the AppImageConfig to describe.
     */
    AppImageConfigName: AppImageConfigName;
  }
  export interface DescribeAppImageConfigResponse {
    /**
     * The ARN of the AppImageConfig.
     */
    AppImageConfigArn?: AppImageConfigArn;
    /**
     * The name of the AppImageConfig.
     */
    AppImageConfigName?: AppImageConfigName;
    /**
     * When the AppImageConfig was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the AppImageConfig was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The configuration of a KernelGateway app.
     */
    KernelGatewayImageConfig?: KernelGatewayImageConfig;
    /**
     * The configuration of the JupyterLab app.
     */
    JupyterLabAppImageConfig?: JupyterLabAppImageConfig;
    /**
     * The configuration of the Code Editor app.
     */
    CodeEditorAppImageConfig?: CodeEditorAppImageConfig;
  }
  export interface DescribeAppRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name. If this value is not set, then SpaceName must be set.
     */
    UserProfileName?: UserProfileName;
    /**
     * The name of the space.
     */
    SpaceName?: SpaceName;
    /**
     * The type of app.
     */
    AppType: AppType;
    /**
     * The name of the app.
     */
    AppName: AppName;
  }
  export interface DescribeAppResponse {
    /**
     * The Amazon Resource Name (ARN) of the app.
     */
    AppArn?: AppArn;
    /**
     * The type of app.
     */
    AppType?: AppType;
    /**
     * The name of the app.
     */
    AppName?: AppName;
    /**
     * The domain ID.
     */
    DomainId?: DomainId;
    /**
     * The user profile name.
     */
    UserProfileName?: UserProfileName;
    /**
     * The name of the space. If this value is not set, then UserProfileName must be set.
     */
    SpaceName?: SpaceName;
    /**
     * The status.
     */
    Status?: AppStatus;
    /**
     * The timestamp of the last health check.
     */
    LastHealthCheckTimestamp?: Timestamp;
    /**
     * The timestamp of the last user's activity. LastUserActivityTimestamp is also updated when SageMaker performs health checks without user activity. As a result, this value is set to the same value as LastHealthCheckTimestamp.
     */
    LastUserActivityTimestamp?: Timestamp;
    /**
     * The creation time of the application.  After an application has been shut down for 24 hours, SageMaker deletes all metadata for the application. To be considered an update and retain application metadata, applications must be restarted within 24 hours after the previous application has been shut down. After this time window, creation of an application is considered a new application rather than an update of the previous application. 
     */
    CreationTime?: Timestamp;
    /**
     * The failure reason.
     */
    FailureReason?: FailureReason;
    /**
     * The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
     */
    ResourceSpec?: ResourceSpec;
  }
  export interface DescribeArtifactRequest {
    /**
     * The Amazon Resource Name (ARN) of the artifact to describe.
     */
    ArtifactArn: ArtifactArn;
  }
  export interface DescribeArtifactResponse {
    /**
     * The name of the artifact.
     */
    ArtifactName?: ExperimentEntityNameOrArn;
    /**
     * The Amazon Resource Name (ARN) of the artifact.
     */
    ArtifactArn?: ArtifactArn;
    /**
     * The source of the artifact.
     */
    Source?: ArtifactSource;
    /**
     * The type of the artifact.
     */
    ArtifactType?: String256;
    /**
     * A list of the artifact's properties.
     */
    Properties?: LineageEntityParameters;
    /**
     * When the artifact was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * When the artifact was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    MetadataProperties?: MetadataProperties;
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
  }
  export interface DescribeAutoMLJobRequest {
    /**
     * Requests information about an AutoML job using its unique name.
     */
    AutoMLJobName: AutoMLJobName;
  }
  export interface DescribeAutoMLJobResponse {
    /**
     * Returns the name of the AutoML job.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * Returns the ARN of the AutoML job.
     */
    AutoMLJobArn: AutoMLJobArn;
    /**
     * Returns the input data configuration for the AutoML job.
     */
    InputDataConfig: AutoMLInputDataConfig;
    /**
     * Returns the job's output data config.
     */
    OutputDataConfig: AutoMLOutputDataConfig;
    /**
     * The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
     */
    RoleArn: RoleArn;
    /**
     * Returns the job's objective.
     */
    AutoMLJobObjective?: AutoMLJobObjective;
    /**
     * Returns the job's problem type.
     */
    ProblemType?: ProblemType;
    /**
     * Returns the configuration for the AutoML job.
     */
    AutoMLJobConfig?: AutoMLJobConfig;
    /**
     * Returns the creation time of the AutoML job.
     */
    CreationTime: Timestamp;
    /**
     * Returns the end time of the AutoML job.
     */
    EndTime?: Timestamp;
    /**
     * Returns the job's last modified time.
     */
    LastModifiedTime: Timestamp;
    /**
     * Returns the failure reason for an AutoML job, when applicable.
     */
    FailureReason?: AutoMLFailureReason;
    /**
     * Returns a list of reasons for partial failures within an AutoML job.
     */
    PartialFailureReasons?: AutoMLPartialFailureReasons;
    /**
     * The best model candidate selected by SageMaker Autopilot using both the best objective metric and lowest InferenceLatency for an experiment.
     */
    BestCandidate?: AutoMLCandidate;
    /**
     * Returns the status of the AutoML job.
     */
    AutoMLJobStatus: AutoMLJobStatus;
    /**
     * Returns the secondary status of the AutoML job.
     */
    AutoMLJobSecondaryStatus: AutoMLJobSecondaryStatus;
    /**
     * Indicates whether the output for an AutoML job generates candidate definitions only.
     */
    GenerateCandidateDefinitionsOnly?: GenerateCandidateDefinitionsOnly;
    /**
     * Returns information on the job's artifacts found in AutoMLJobArtifacts.
     */
    AutoMLJobArtifacts?: AutoMLJobArtifacts;
    /**
     * Contains ProblemType, AutoMLJobObjective, and CompletionCriteria. If you do not provide these values, they are inferred.
     */
    ResolvedAttributes?: ResolvedAttributes;
    /**
     * Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
     */
    ModelDeployConfig?: ModelDeployConfig;
    /**
     * Provides information about endpoint for the model deployment.
     */
    ModelDeployResult?: ModelDeployResult;
  }
  export interface DescribeAutoMLJobV2Request {
    /**
     * Requests information about an AutoML job V2 using its unique name.
     */
    AutoMLJobName: AutoMLJobName;
  }
  export interface DescribeAutoMLJobV2Response {
    /**
     * Returns the name of the AutoML job V2.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * Returns the Amazon Resource Name (ARN) of the AutoML job V2.
     */
    AutoMLJobArn: AutoMLJobArn;
    /**
     * Returns an array of channel objects describing the input data and their location.
     */
    AutoMLJobInputDataConfig: AutoMLJobInputDataConfig;
    /**
     * Returns the job's output data config.
     */
    OutputDataConfig: AutoMLOutputDataConfig;
    /**
     * The ARN of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
     */
    RoleArn: RoleArn;
    /**
     * Returns the job's objective.
     */
    AutoMLJobObjective?: AutoMLJobObjective;
    /**
     * Returns the configuration settings of the problem type set for the AutoML job V2.
     */
    AutoMLProblemTypeConfig?: AutoMLProblemTypeConfig;
    /**
     * Returns the name of the problem type configuration set for the AutoML job V2.
     */
    AutoMLProblemTypeConfigName?: AutoMLProblemTypeConfigName;
    /**
     * Returns the creation time of the AutoML job V2.
     */
    CreationTime: Timestamp;
    /**
     * Returns the end time of the AutoML job V2.
     */
    EndTime?: Timestamp;
    /**
     * Returns the job's last modified time.
     */
    LastModifiedTime: Timestamp;
    /**
     * Returns the reason for the failure of the AutoML job V2, when applicable.
     */
    FailureReason?: AutoMLFailureReason;
    /**
     * Returns a list of reasons for partial failures within an AutoML job V2.
     */
    PartialFailureReasons?: AutoMLPartialFailureReasons;
    /**
     * Information about the candidate produced by an AutoML training job V2, including its status, steps, and other properties.
     */
    BestCandidate?: AutoMLCandidate;
    /**
     * Returns the status of the AutoML job V2.
     */
    AutoMLJobStatus: AutoMLJobStatus;
    /**
     * Returns the secondary status of the AutoML job V2.
     */
    AutoMLJobSecondaryStatus: AutoMLJobSecondaryStatus;
    AutoMLJobArtifacts?: AutoMLJobArtifacts;
    /**
     * Returns the resolved attributes used by the AutoML job V2.
     */
    ResolvedAttributes?: AutoMLResolvedAttributes;
    /**
     * Indicates whether the model was deployed automatically to an endpoint and the name of that endpoint if deployed automatically.
     */
    ModelDeployConfig?: ModelDeployConfig;
    /**
     * Provides information about endpoint for the model deployment.
     */
    ModelDeployResult?: ModelDeployResult;
    /**
     * Returns the configuration settings of how the data are split into train and validation datasets.
     */
    DataSplitConfig?: AutoMLDataSplitConfig;
    /**
     * Returns the security configuration for traffic encryption or Amazon VPC settings.
     */
    SecurityConfig?: AutoMLSecurityConfig;
    /**
     * The compute configuration used for the AutoML job V2.
     */
    AutoMLComputeConfig?: AutoMLComputeConfig;
  }
  export interface DescribeClusterNodeRequest {
    /**
     * The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which the node is.
     */
    ClusterName: ClusterNameOrArn;
    /**
     * The ID of the SageMaker HyperPod cluster node.
     */
    NodeId: ClusterNodeId;
  }
  export interface DescribeClusterNodeResponse {
    /**
     * The details of the SageMaker HyperPod cluster node.
     */
    NodeDetails: ClusterNodeDetails;
  }
  export interface DescribeClusterRequest {
    /**
     * The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
     */
    ClusterName: ClusterNameOrArn;
  }
  export interface DescribeClusterResponse {
    /**
     * The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster.
     */
    ClusterArn: ClusterArn;
    /**
     * The name of the SageMaker HyperPod cluster.
     */
    ClusterName?: ClusterName;
    /**
     * The status of the SageMaker HyperPod cluster.
     */
    ClusterStatus: ClusterStatus;
    /**
     * The time when the SageMaker Cluster is created.
     */
    CreationTime?: Timestamp;
    /**
     * The failure message of the SageMaker HyperPod cluster.
     */
    FailureMessage?: String;
    /**
     * The instance groups of the SageMaker HyperPod cluster.
     */
    InstanceGroups: ClusterInstanceGroupDetailsList;
    VpcConfig?: VpcConfig;
  }
  export interface DescribeCodeRepositoryInput {
    /**
     * The name of the Git repository to describe.
     */
    CodeRepositoryName: EntityName;
  }
  export interface DescribeCodeRepositoryOutput {
    /**
     * The name of the Git repository.
     */
    CodeRepositoryName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the Git repository.
     */
    CodeRepositoryArn: CodeRepositoryArn;
    /**
     * The date and time that the repository was created.
     */
    CreationTime: CreationTime;
    /**
     * The date and time that the repository was last changed.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * Configuration details about the repository, including the URL where the repository is located, the default branch, and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.
     */
    GitConfig?: GitConfig;
  }
  export interface DescribeCompilationJobRequest {
    /**
     * The name of the model compilation job that you want information about.
     */
    CompilationJobName: EntityName;
  }
  export interface DescribeCompilationJobResponse {
    /**
     * The name of the model compilation job.
     */
    CompilationJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model compilation job.
     */
    CompilationJobArn: CompilationJobArn;
    /**
     * The status of the model compilation job.
     */
    CompilationJobStatus: CompilationJobStatus;
    /**
     * The time when the model compilation job started the CompilationJob instances.  You are billed for the time between this timestamp and the timestamp in the CompilationEndTime field. In Amazon CloudWatch Logs, the start time might be later than this time. That's because it takes time to download the compilation job, which depends on the size of the compilation job container. 
     */
    CompilationStartTime?: Timestamp;
    /**
     * The time when the model compilation job on a compilation job instance ended. For a successful or stopped job, this is when the job's model artifacts have finished uploading. For a failed job, this is when Amazon SageMaker detected that the job failed. 
     */
    CompilationEndTime?: Timestamp;
    /**
     * Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker ends the compilation job. Use this API to cap model training costs.
     */
    StoppingCondition: StoppingCondition;
    /**
     * The inference image to use when compiling a model. Specify an image only if the target device is a cloud instance.
     */
    InferenceImage?: InferenceImage;
    /**
     * The Amazon Resource Name (ARN) of the versioned model package that was provided to SageMaker Neo when you initiated a compilation job.
     */
    ModelPackageVersionArn?: ModelPackageArn;
    /**
     * The time that the model compilation job was created.
     */
    CreationTime: CreationTime;
    /**
     * The time that the status of the model compilation job was last modified.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * If a model compilation job failed, the reason it failed. 
     */
    FailureReason: FailureReason;
    /**
     * Information about the location in Amazon S3 that has been configured for storing the model artifacts used in the compilation job.
     */
    ModelArtifacts: ModelArtifacts;
    /**
     * Provides a BLAKE2 hash value that identifies the compiled model artifacts in Amazon S3.
     */
    ModelDigests?: ModelDigests;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker assumes to perform the model compilation job.
     */
    RoleArn: RoleArn;
    /**
     * Information about the location in Amazon S3 of the input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
     */
    InputConfig: InputConfig;
    /**
     * Information about the output location for the compiled model and the target device that the model runs on.
     */
    OutputConfig: OutputConfig;
    /**
     * A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: NeoVpcConfig;
    /**
     * Information that SageMaker Neo automatically derived about the model.
     */
    DerivedInformation?: DerivedInformation;
  }
  export interface DescribeContextRequest {
    /**
     * The name of the context to describe.
     */
    ContextName: ContextNameOrArn;
  }
  export interface DescribeContextResponse {
    /**
     * The name of the context.
     */
    ContextName?: ContextName;
    /**
     * The Amazon Resource Name (ARN) of the context.
     */
    ContextArn?: ContextArn;
    /**
     * The source of the context.
     */
    Source?: ContextSource;
    /**
     * The type of the context.
     */
    ContextType?: String256;
    /**
     * The description of the context.
     */
    Description?: ExperimentDescription;
    /**
     * A list of the context's properties.
     */
    Properties?: LineageEntityParameters;
    /**
     * When the context was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * When the context was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
  }
  export interface DescribeDataQualityJobDefinitionRequest {
    /**
     * The name of the data quality monitoring job definition to describe.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DescribeDataQualityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the data quality monitoring job definition.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
    /**
     * The name of the data quality monitoring job definition.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The time that the data quality monitoring job definition was created.
     */
    CreationTime: Timestamp;
    /**
     * The constraints and baselines for the data quality monitoring job definition.
     */
    DataQualityBaselineConfig?: DataQualityBaselineConfig;
    /**
     * Information about the container that runs the data quality monitoring job.
     */
    DataQualityAppSpecification: DataQualityAppSpecification;
    /**
     * The list of inputs for the data quality monitoring job. Currently endpoints are supported.
     */
    DataQualityJobInput: DataQualityJobInput;
    DataQualityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * The networking configuration for the data quality monitoring job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
  }
  export interface DescribeDeviceFleetRequest {
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
  }
  export interface DescribeDeviceFleetResponse {
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
    /**
     * The The Amazon Resource Name (ARN) of the fleet.
     */
    DeviceFleetArn: DeviceFleetArn;
    /**
     * The output configuration for storing sampled data.
     */
    OutputConfig: EdgeOutputConfig;
    /**
     * A description of the fleet.
     */
    Description?: DeviceFleetDescription;
    /**
     * Timestamp of when the device fleet was created.
     */
    CreationTime: Timestamp;
    /**
     * Timestamp of when the device fleet was last updated.
     */
    LastModifiedTime: Timestamp;
    /**
     * The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).
     */
    RoleArn?: RoleArn;
    /**
     * The Amazon Resource Name (ARN) alias created in Amazon Web Services Internet of Things (IoT).
     */
    IotRoleAlias?: IotRoleAlias;
  }
  export interface DescribeDeviceRequest {
    /**
     * Next token of device description.
     */
    NextToken?: NextToken;
    /**
     * The unique ID of the device.
     */
    DeviceName: EntityName;
    /**
     * The name of the fleet the devices belong to.
     */
    DeviceFleetName: EntityName;
  }
  export interface DescribeDeviceResponse {
    /**
     * The Amazon Resource Name (ARN) of the device.
     */
    DeviceArn?: DeviceArn;
    /**
     * The unique identifier of the device.
     */
    DeviceName: EntityName;
    /**
     * A description of the device.
     */
    Description?: DeviceDescription;
    /**
     * The name of the fleet the device belongs to.
     */
    DeviceFleetName: EntityName;
    /**
     * The Amazon Web Services Internet of Things (IoT) object thing name associated with the device.
     */
    IotThingName?: ThingName;
    /**
     * The timestamp of the last registration or de-reregistration.
     */
    RegistrationTime: Timestamp;
    /**
     * The last heartbeat received from the device.
     */
    LatestHeartbeat?: Timestamp;
    /**
     * Models on the device.
     */
    Models?: EdgeModels;
    /**
     * The maximum number of models.
     */
    MaxModels?: Integer;
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
    /**
     * Edge Manager agent version.
     */
    AgentVersion?: EdgeVersion;
  }
  export interface DescribeDomainRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
  }
  export interface DescribeDomainResponse {
    /**
     * The domain's Amazon Resource Name (ARN).
     */
    DomainArn?: DomainArn;
    /**
     * The domain ID.
     */
    DomainId?: DomainId;
    /**
     * The domain name.
     */
    DomainName?: DomainName;
    /**
     * The ID of the Amazon Elastic File System managed by this Domain.
     */
    HomeEfsFileSystemId?: ResourceId;
    /**
     * The IAM Identity Center managed application instance ID.
     */
    SingleSignOnManagedApplicationInstanceId?: String256;
    /**
     * The ARN of the application managed by SageMaker in IAM Identity Center. This value is only returned for domains created after October 1, 2023.
     */
    SingleSignOnApplicationArn?: SingleSignOnApplicationArn;
    /**
     * The status.
     */
    Status?: DomainStatus;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The failure reason.
     */
    FailureReason?: FailureReason;
    /**
     * The ID of the security group that authorizes traffic between the RSessionGateway apps and the RStudioServerPro app.
     */
    SecurityGroupIdForDomainBoundary?: SecurityGroupId;
    /**
     * The domain's authentication mode.
     */
    AuthMode?: AuthMode;
    /**
     * Settings which are applied to UserProfiles in this domain if settings are not explicitly specified in a given UserProfile. 
     */
    DefaultUserSettings?: UserSettings;
    /**
     * A collection of Domain settings.
     */
    DomainSettings?: DomainSettings;
    /**
     * Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.    PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access    VpcOnly - All traffic is through the specified VPC and subnets  
     */
    AppNetworkAccessType?: AppNetworkAccessType;
    /**
     * Use KmsKeyId.
     */
    HomeEfsFileSystemKmsKeyId?: KmsKeyId;
    /**
     * The VPC subnets that the domain uses for communication.
     */
    SubnetIds?: Subnets;
    /**
     * The domain's URL.
     */
    Url?: String1024;
    /**
     * The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.
     */
    VpcId?: VpcId;
    /**
     * The Amazon Web Services KMS customer managed key used to encrypt the EFS volume attached to the domain.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided.
     */
    AppSecurityGroupManagement?: AppSecurityGroupManagement;
    /**
     * The default settings used to create a space.
     */
    DefaultSpaceSettings?: DefaultSpaceSettings;
  }
  export interface DescribeEdgeDeploymentPlanRequest {
    /**
     * The name of the deployment plan to describe.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * If the edge deployment plan has enough stages to require tokening, then this is the response from the last list of stages returned.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to select (50 by default).
     */
    MaxResults?: DeploymentStageMaxResults;
  }
  export interface DescribeEdgeDeploymentPlanResponse {
    /**
     * The ARN of edge deployment plan.
     */
    EdgeDeploymentPlanArn: EdgeDeploymentPlanArn;
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * List of models associated with the edge deployment plan.
     */
    ModelConfigs: EdgeDeploymentModelConfigs;
    /**
     * The device fleet used for this edge deployment plan.
     */
    DeviceFleetName: EntityName;
    /**
     * The number of edge devices with the successful deployment.
     */
    EdgeDeploymentSuccess?: Integer;
    /**
     * The number of edge devices yet to pick up deployment, or in progress.
     */
    EdgeDeploymentPending?: Integer;
    /**
     * The number of edge devices that failed the deployment.
     */
    EdgeDeploymentFailed?: Integer;
    /**
     * List of stages in the edge deployment plan.
     */
    Stages: DeploymentStageStatusSummaries;
    /**
     * Token to use when calling the next set of stages in the edge deployment plan.
     */
    NextToken?: NextToken;
    /**
     * The time when the edge deployment plan was created.
     */
    CreationTime?: Timestamp;
    /**
     * The time when the edge deployment plan was last updated.
     */
    LastModifiedTime?: Timestamp;
  }
  export interface DescribeEdgePackagingJobRequest {
    /**
     * The name of the edge packaging job.
     */
    EdgePackagingJobName: EntityName;
  }
  export interface DescribeEdgePackagingJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the edge packaging job.
     */
    EdgePackagingJobArn: EdgePackagingJobArn;
    /**
     * The name of the edge packaging job.
     */
    EdgePackagingJobName: EntityName;
    /**
     * The name of the SageMaker Neo compilation job that is used to locate model artifacts that are being packaged.
     */
    CompilationJobName?: EntityName;
    /**
     * The name of the model.
     */
    ModelName?: EntityName;
    /**
     * The version of the model.
     */
    ModelVersion?: EdgeVersion;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact Neo.
     */
    RoleArn?: RoleArn;
    /**
     * The output configuration for the edge packaging job.
     */
    OutputConfig?: EdgeOutputConfig;
    /**
     * The Amazon Web Services KMS key to use when encrypting the EBS volume the job run on.
     */
    ResourceKey?: KmsKeyId;
    /**
     * The current status of the packaging job.
     */
    EdgePackagingJobStatus: EdgePackagingJobStatus;
    /**
     * Returns a message describing the job status and error messages.
     */
    EdgePackagingJobStatusMessage?: String;
    /**
     * The timestamp of when the packaging job was created.
     */
    CreationTime?: Timestamp;
    /**
     * The timestamp of when the job was last updated.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The Amazon Simple Storage (S3) URI where model artifacts ares stored.
     */
    ModelArtifact?: S3Uri;
    /**
     * The signature document of files in the model artifact.
     */
    ModelSignature?: String;
    /**
     * The output of a SageMaker Edge Manager deployable resource.
     */
    PresetDeploymentOutput?: EdgePresetDeploymentOutput;
  }
  export interface DescribeEndpointConfigInput {
    /**
     * The name of the endpoint configuration.
     */
    EndpointConfigName: EndpointConfigName;
  }
  export interface DescribeEndpointConfigOutput {
    /**
     * Name of the SageMaker endpoint configuration.
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint configuration.
     */
    EndpointConfigArn: EndpointConfigArn;
    /**
     * An array of ProductionVariant objects, one for each model that you want to host at this endpoint.
     */
    ProductionVariants: ProductionVariantList;
    DataCaptureConfig?: DataCaptureConfig;
    /**
     * Amazon Web Services KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * A timestamp that shows when the endpoint configuration was created.
     */
    CreationTime: Timestamp;
    /**
     * Returns the description of an endpoint configuration created using the  CreateEndpointConfig  API.
     */
    AsyncInferenceConfig?: AsyncInferenceConfig;
    /**
     * The configuration parameters for an explainer.
     */
    ExplainerConfig?: ExplainerConfig;
    /**
     * An array of ProductionVariant objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.
     */
    ShadowProductionVariants?: ProductionVariantList;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that you assigned to the endpoint configuration.
     */
    ExecutionRoleArn?: RoleArn;
    VpcConfig?: VpcConfig;
    /**
     * Indicates whether all model containers deployed to the endpoint are isolated. If they are, no inbound or outbound network calls can be made to or from the model containers.
     */
    EnableNetworkIsolation?: Boolean;
  }
  export interface DescribeEndpointInput {
    /**
     * The name of the endpoint.
     */
    EndpointName: EndpointName;
  }
  export interface DescribeEndpointOutput {
    /**
     * Name of the endpoint.
     */
    EndpointName: EndpointName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
    /**
     * The name of the endpoint configuration associated with this endpoint.
     */
    EndpointConfigName?: EndpointConfigName;
    /**
     * An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.
     */
    ProductionVariants?: ProductionVariantSummaryList;
    DataCaptureConfig?: DataCaptureConfigSummary;
    /**
     * The status of the endpoint.    OutOfService: Endpoint is not available to take incoming requests.    Creating: CreateEndpoint is executing.    Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.    SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.    RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.    InService: Endpoint is available to process incoming requests.    Deleting: DeleteEndpoint is executing.    Failed: Endpoint could not be created, updated, or re-scaled. Use the FailureReason value returned by DescribeEndpoint for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.    UpdateRollbackFailed: Both the rolling deployment and auto-rollback failed. Your endpoint is in service with a mix of the old and new endpoint configurations. For information about how to remedy this issue and restore the endpoint's status to InService, see Rolling Deployments.  
     */
    EndpointStatus: EndpointStatus;
    /**
     * If the status of the endpoint is Failed, the reason why it failed. 
     */
    FailureReason?: FailureReason;
    /**
     * A timestamp that shows when the endpoint was created.
     */
    CreationTime: Timestamp;
    /**
     * A timestamp that shows when the endpoint was last modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The most recent deployment configuration for the endpoint.
     */
    LastDeploymentConfig?: DeploymentConfig;
    /**
     * Returns the description of an endpoint configuration created using the  CreateEndpointConfig  API.
     */
    AsyncInferenceConfig?: AsyncInferenceConfig;
    /**
     * Returns the summary of an in-progress deployment. This field is only returned when the endpoint is creating or updating with a new endpoint configuration.
     */
    PendingDeploymentSummary?: PendingDeploymentSummary;
    /**
     * The configuration parameters for an explainer.
     */
    ExplainerConfig?: ExplainerConfig;
    /**
     * An array of ProductionVariantSummary objects, one for each model that you want to host at this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants.
     */
    ShadowProductionVariants?: ProductionVariantSummaryList;
  }
  export interface DescribeExperimentRequest {
    /**
     * The name of the experiment to describe.
     */
    ExperimentName: ExperimentEntityName;
  }
  export interface DescribeExperimentResponse {
    /**
     * The name of the experiment.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the experiment.
     */
    ExperimentArn?: ExperimentArn;
    /**
     * The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the source and, optionally, the type.
     */
    Source?: ExperimentSource;
    /**
     * The description of the experiment.
     */
    Description?: ExperimentDescription;
    /**
     * When the experiment was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the experiment.
     */
    CreatedBy?: UserContext;
    /**
     * When the experiment was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * Who last modified the experiment.
     */
    LastModifiedBy?: UserContext;
  }
  export interface DescribeFeatureGroupRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the FeatureGroup you want described. 
     */
    FeatureGroupName: FeatureGroupNameOrArn;
    /**
     * A token to resume pagination of the list of Features (FeatureDefinitions). 2,500 Features are returned by default.
     */
    NextToken?: NextToken;
  }
  export interface DescribeFeatureGroupResponse {
    /**
     * The Amazon Resource Name (ARN) of the FeatureGroup. 
     */
    FeatureGroupArn: FeatureGroupArn;
    /**
     * he name of the FeatureGroup.
     */
    FeatureGroupName: FeatureGroupName;
    /**
     * The name of the Feature used for RecordIdentifier, whose value uniquely identifies a record stored in the feature store.
     */
    RecordIdentifierFeatureName: FeatureName;
    /**
     * The name of the feature that stores the EventTime of a Record in a FeatureGroup.  An EventTime is a point in time when a new event occurs that corresponds to the creation or update of a Record in a FeatureGroup. All Records in the FeatureGroup have a corresponding EventTime.
     */
    EventTimeFeatureName: FeatureName;
    /**
     * A list of the Features in the FeatureGroup. Each feature is defined by a FeatureName and FeatureType.
     */
    FeatureDefinitions: FeatureDefinitions;
    /**
     * A timestamp indicating when SageMaker created the FeatureGroup.
     */
    CreationTime: CreationTime;
    /**
     * A timestamp indicating when the feature group was last updated.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The configuration for the OnlineStore.
     */
    OnlineStoreConfig?: OnlineStoreConfig;
    /**
     * The configuration of the offline store. It includes the following configurations:   Amazon S3 location of the offline store.   Configuration of the Glue data catalog.   Table format of the offline store.   Option to disable the automatic creation of a Glue table for the offline store.   Encryption configuration.  
     */
    OfflineStoreConfig?: OfflineStoreConfig;
    ThroughputConfig?: ThroughputConfigDescription;
    /**
     * The Amazon Resource Name (ARN) of the IAM execution role used to persist data into the OfflineStore if an OfflineStoreConfig is provided.
     */
    RoleArn?: RoleArn;
    /**
     * The status of the feature group.
     */
    FeatureGroupStatus?: FeatureGroupStatus;
    /**
     * The status of the OfflineStore. Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked 
     */
    OfflineStoreStatus?: OfflineStoreStatus;
    /**
     * A value indicating whether the update made to the feature group was successful.
     */
    LastUpdateStatus?: LastUpdateStatus;
    /**
     * The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure can occur because:   The FeatureGroup could not be created in the OfflineStore.   The FeatureGroup could not be deleted from the OfflineStore.  
     */
    FailureReason?: FailureReason;
    /**
     * A free form description of the feature group.
     */
    Description?: Description;
    /**
     * A token to resume pagination of the list of Features (FeatureDefinitions).
     */
    NextToken: NextToken;
    /**
     * The size of the OnlineStore in bytes.
     */
    OnlineStoreTotalSizeBytes?: OnlineStoreTotalSizeBytes;
  }
  export interface DescribeFeatureMetadataRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the feature group containing the feature.
     */
    FeatureGroupName: FeatureGroupNameOrArn;
    /**
     * The name of the feature.
     */
    FeatureName: FeatureName;
  }
  export interface DescribeFeatureMetadataResponse {
    /**
     * The Amazon Resource Number (ARN) of the feature group that contains the feature.
     */
    FeatureGroupArn: FeatureGroupArn;
    /**
     * The name of the feature group that you've specified.
     */
    FeatureGroupName: FeatureGroupName;
    /**
     * The name of the feature that you've specified.
     */
    FeatureName: FeatureName;
    /**
     * The data type of the feature.
     */
    FeatureType: FeatureType;
    /**
     * A timestamp indicating when the feature was created.
     */
    CreationTime: CreationTime;
    /**
     * A timestamp indicating when the metadata for the feature group was modified. For example, if you add a parameter describing the feature, the timestamp changes to reflect the last time you 
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * The description you added to describe the feature.
     */
    Description?: FeatureDescription;
    /**
     * The key-value pairs that you added to describe the feature.
     */
    Parameters?: FeatureParameters;
  }
  export interface DescribeFlowDefinitionRequest {
    /**
     * The name of the flow definition.
     */
    FlowDefinitionName: FlowDefinitionName;
  }
  export interface DescribeFlowDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the flow defintion.
     */
    FlowDefinitionArn: FlowDefinitionArn;
    /**
     * The Amazon Resource Name (ARN) of the flow definition.
     */
    FlowDefinitionName: FlowDefinitionName;
    /**
     * The status of the flow definition. Valid values are listed below.
     */
    FlowDefinitionStatus: FlowDefinitionStatus;
    /**
     * The timestamp when the flow definition was created.
     */
    CreationTime: Timestamp;
    /**
     * Container for configuring the source of human task requests. Used to specify if Amazon Rekognition or Amazon Textract is used as an integration source.
     */
    HumanLoopRequestSource?: HumanLoopRequestSource;
    /**
     * An object containing information about what triggers a human review workflow.
     */
    HumanLoopActivationConfig?: HumanLoopActivationConfig;
    /**
     * An object containing information about who works on the task, the workforce task price, and other task details.
     */
    HumanLoopConfig?: HumanLoopConfig;
    /**
     * An object containing information about the output file.
     */
    OutputConfig: FlowDefinitionOutputConfig;
    /**
     * The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) execution role for the flow definition.
     */
    RoleArn: RoleArn;
    /**
     * The reason your flow definition failed.
     */
    FailureReason?: FailureReason;
  }
  export interface DescribeHubContentRequest {
    /**
     * The name of the hub that contains the content to describe.
     */
    HubName: HubNameOrArn;
    /**
     * The type of content in the hub.
     */
    HubContentType: HubContentType;
    /**
     * The name of the content to describe.
     */
    HubContentName: HubContentName;
    /**
     * The version of the content to describe.
     */
    HubContentVersion?: HubContentVersion;
  }
  export interface DescribeHubContentResponse {
    /**
     * The name of the hub content.
     */
    HubContentName: HubContentName;
    /**
     * The Amazon Resource Name (ARN) of the hub content.
     */
    HubContentArn: HubContentArn;
    /**
     * The version of the hub content.
     */
    HubContentVersion: HubContentVersion;
    /**
     * The type of hub content.
     */
    HubContentType: HubContentType;
    /**
     * The document schema version for the hub content.
     */
    DocumentSchemaVersion: DocumentSchemaVersion;
    /**
     * The name of the hub that contains the content.
     */
    HubName: HubName;
    /**
     * The Amazon Resource Name (ARN) of the hub that contains the content. 
     */
    HubArn: HubArn;
    /**
     * The display name of the hub content.
     */
    HubContentDisplayName?: HubContentDisplayName;
    /**
     * A description of the hub content.
     */
    HubContentDescription?: HubContentDescription;
    /**
     * A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
     */
    HubContentMarkdown?: HubContentMarkdown;
    /**
     * The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
     */
    HubContentDocument: HubContentDocument;
    /**
     * The ARN of the public hub content.
     */
    SageMakerPublicHubContentArn?: SageMakerPublicHubContentArn;
    /**
     * The minimum version of the hub content.
     */
    ReferenceMinVersion?: ReferenceMinVersion;
    /**
     * The support status of the hub content.
     */
    SupportStatus?: HubContentSupportStatus;
    /**
     * The searchable keywords for the hub content.
     */
    HubContentSearchKeywords?: HubContentSearchKeywordList;
    /**
     * The location of any dependencies that the hub content has, such as scripts, model artifacts, datasets, or notebooks.
     */
    HubContentDependencies?: HubContentDependencyList;
    /**
     * The status of the hub content.
     */
    HubContentStatus: HubContentStatus;
    /**
     * The failure reason if importing hub content failed.
     */
    FailureReason?: FailureReason;
    /**
     * The date and time that hub content was created.
     */
    CreationTime: Timestamp;
  }
  export interface DescribeHubRequest {
    /**
     * The name of the hub to describe.
     */
    HubName: HubNameOrArn;
  }
  export interface DescribeHubResponse {
    /**
     * The name of the hub.
     */
    HubName: HubName;
    /**
     * The Amazon Resource Name (ARN) of the hub.
     */
    HubArn: HubArn;
    /**
     * The display name of the hub.
     */
    HubDisplayName?: HubDisplayName;
    /**
     * A description of the hub.
     */
    HubDescription?: HubDescription;
    /**
     * The searchable keywords for the hub.
     */
    HubSearchKeywords?: HubSearchKeywordList;
    /**
     * The Amazon S3 storage configuration for the hub.
     */
    S3StorageConfig?: HubS3StorageConfig;
    /**
     * The status of the hub.
     */
    HubStatus: HubStatus;
    /**
     * The failure reason if importing hub content failed.
     */
    FailureReason?: FailureReason;
    /**
     * The date and time that the hub was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the hub was last modified.
     */
    LastModifiedTime: Timestamp;
  }
  export interface DescribeHumanTaskUiRequest {
    /**
     * The name of the human task user interface (worker task template) you want information about.
     */
    HumanTaskUiName: HumanTaskUiName;
  }
  export interface DescribeHumanTaskUiResponse {
    /**
     * The Amazon Resource Name (ARN) of the human task user interface (worker task template).
     */
    HumanTaskUiArn: HumanTaskUiArn;
    /**
     * The name of the human task user interface (worker task template).
     */
    HumanTaskUiName: HumanTaskUiName;
    /**
     * The status of the human task user interface (worker task template). Valid values are listed below.
     */
    HumanTaskUiStatus?: HumanTaskUiStatus;
    /**
     * The timestamp when the human task user interface was created.
     */
    CreationTime: Timestamp;
    UiTemplate: UiTemplateInfo;
  }
  export interface DescribeHyperParameterTuningJobRequest {
    /**
     * The name of the tuning job.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
  }
  export interface DescribeHyperParameterTuningJobResponse {
    /**
     * The name of the hyperparameter tuning job.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
    /**
     * The Amazon Resource Name (ARN) of the tuning job.
     */
    HyperParameterTuningJobArn: HyperParameterTuningJobArn;
    /**
     * The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.
     */
    HyperParameterTuningJobConfig: HyperParameterTuningJobConfig;
    /**
     * The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.
     */
    TrainingJobDefinition?: HyperParameterTrainingJobDefinition;
    /**
     * A list of the HyperParameterTrainingJobDefinition objects launched for this tuning job.
     */
    TrainingJobDefinitions?: HyperParameterTrainingJobDefinitions;
    /**
     * The status of the tuning job.
     */
    HyperParameterTuningJobStatus: HyperParameterTuningJobStatus;
    /**
     * The date and time that the tuning job started.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the tuning job ended.
     */
    HyperParameterTuningEndTime?: Timestamp;
    /**
     * The date and time that the status of the tuning job was modified. 
     */
    LastModifiedTime?: Timestamp;
    /**
     * The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.
     */
    TrainingJobStatusCounters: TrainingJobStatusCounters;
    /**
     * The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.
     */
    ObjectiveStatusCounters: ObjectiveStatusCounters;
    /**
     * A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.
     */
    BestTrainingJob?: HyperParameterTrainingJobSummary;
    /**
     * If the hyperparameter tuning job is an warm start tuning job with a WarmStartType of IDENTICAL_DATA_AND_ALGORITHM, this is the TrainingJobSummary for the training job with the best objective metric value of all training jobs launched by this tuning job and all parent jobs specified for the warm start tuning job.
     */
    OverallBestTrainingJob?: HyperParameterTrainingJobSummary;
    /**
     * The configuration for starting the hyperparameter parameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.
     */
    WarmStartConfig?: HyperParameterTuningJobWarmStartConfig;
    /**
     * A flag to indicate if autotune is enabled for the hyperparameter tuning job.
     */
    Autotune?: Autotune;
    /**
     * If the tuning job failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * Tuning job completion information returned as the response from a hyperparameter tuning job. This information tells if your tuning job has or has not converged. It also includes the number of training jobs that have not improved model performance as evaluated against the objective function.
     */
    TuningJobCompletionDetails?: HyperParameterTuningJobCompletionDetails;
    ConsumedResources?: HyperParameterTuningJobConsumedResources;
  }
  export interface DescribeImageRequest {
    /**
     * The name of the image to describe.
     */
    ImageName: ImageName;
  }
  export interface DescribeImageResponse {
    /**
     * When the image was created.
     */
    CreationTime?: Timestamp;
    /**
     * The description of the image.
     */
    Description?: ImageDescription;
    /**
     * The name of the image as displayed.
     */
    DisplayName?: ImageDisplayName;
    /**
     * When a create, update, or delete operation fails, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * The ARN of the image.
     */
    ImageArn?: ImageArn;
    /**
     * The name of the image.
     */
    ImageName?: ImageName;
    /**
     * The status of the image.
     */
    ImageStatus?: ImageStatus;
    /**
     * When the image was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The ARN of the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
     */
    RoleArn?: RoleArn;
  }
  export interface DescribeImageVersionRequest {
    /**
     * The name of the image.
     */
    ImageName: ImageName;
    /**
     * The version of the image. If not specified, the latest version is described.
     */
    Version?: ImageVersionNumber;
    /**
     * The alias of the image version.
     */
    Alias?: SageMakerImageVersionAlias;
  }
  export interface DescribeImageVersionResponse {
    /**
     * The registry path of the container image on which this image version is based.
     */
    BaseImage?: ImageBaseImage;
    /**
     * The registry path of the container image that contains this image version.
     */
    ContainerImage?: ImageContainerImage;
    /**
     * When the version was created.
     */
    CreationTime?: Timestamp;
    /**
     * When a create or delete operation fails, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * The ARN of the image the version is based on.
     */
    ImageArn?: ImageArn;
    /**
     * The ARN of the version.
     */
    ImageVersionArn?: ImageVersionArn;
    /**
     * The status of the version.
     */
    ImageVersionStatus?: ImageVersionStatus;
    /**
     * When the version was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The version number.
     */
    Version?: ImageVersionNumber;
    /**
     * The stability of the image version specified by the maintainer.    NOT_PROVIDED: The maintainers did not provide a status for image version stability.    STABLE: The image version is stable.    TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.    ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.   
     */
    VendorGuidance?: VendorGuidance;
    /**
     * Indicates SageMaker job type compatibility.    TRAINING: The image version is compatible with SageMaker training jobs.    INFERENCE: The image version is compatible with SageMaker inference jobs.    NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.  
     */
    JobType?: JobType;
    /**
     * The machine learning framework vended in the image version.
     */
    MLFramework?: MLFramework;
    /**
     * The supported programming language and its version.
     */
    ProgrammingLang?: ProgrammingLang;
    /**
     * Indicates CPU or GPU compatibility.    CPU: The image version is compatible with CPU.    GPU: The image version is compatible with GPU.  
     */
    Processor?: Processor;
    /**
     * Indicates Horovod compatibility.
     */
    Horovod?: Horovod;
    /**
     * The maintainer description of the image version.
     */
    ReleaseNotes?: ReleaseNotes;
  }
  export interface DescribeInferenceComponentInput {
    /**
     * The name of the inference component.
     */
    InferenceComponentName: InferenceComponentName;
  }
  export interface DescribeInferenceComponentOutput {
    /**
     * The name of the inference component.
     */
    InferenceComponentName: InferenceComponentName;
    /**
     * The Amazon Resource Name (ARN) of the inference component.
     */
    InferenceComponentArn: InferenceComponentArn;
    /**
     * The name of the endpoint that hosts the inference component.
     */
    EndpointName: EndpointName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
     */
    EndpointArn: EndpointArn;
    /**
     * The name of the production variant that hosts the inference component.
     */
    VariantName?: VariantName;
    /**
     * If the inference component status is Failed, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * Details about the resources that are deployed with this inference component.
     */
    Specification?: InferenceComponentSpecificationSummary;
    /**
     * Details about the runtime settings for the model that is deployed with the inference component.
     */
    RuntimeConfig?: InferenceComponentRuntimeConfigSummary;
    /**
     * The time when the inference component was created.
     */
    CreationTime: Timestamp;
    /**
     * The time when the inference component was last updated.
     */
    LastModifiedTime: Timestamp;
    /**
     * The status of the inference component.
     */
    InferenceComponentStatus?: InferenceComponentStatus;
  }
  export interface DescribeInferenceExperimentRequest {
    /**
     * The name of the inference experiment to describe.
     */
    Name: InferenceExperimentName;
  }
  export interface DescribeInferenceExperimentResponse {
    /**
     * The ARN of the inference experiment being described.
     */
    Arn: InferenceExperimentArn;
    /**
     * The name of the inference experiment.
     */
    Name: InferenceExperimentName;
    /**
     * The type of the inference experiment.
     */
    Type: InferenceExperimentType;
    /**
     * The duration for which the inference experiment ran or will run.
     */
    Schedule?: InferenceExperimentSchedule;
    /**
     *  The status of the inference experiment. The following are the possible statuses for an inference experiment:     Creating - Amazon SageMaker is creating your experiment.     Created - Amazon SageMaker has finished the creation of your experiment and will begin the experiment at the scheduled time.     Updating - When you make changes to your experiment, your experiment shows as updating.     Starting - Amazon SageMaker is beginning your experiment.     Running - Your experiment is in progress.     Stopping - Amazon SageMaker is stopping your experiment.     Completed - Your experiment has completed.     Cancelled - When you conclude your experiment early using the StopInferenceExperiment API, or if any operation fails with an unexpected error, it shows as cancelled.   
     */
    Status: InferenceExperimentStatus;
    /**
     *  The error message or client-specified Reason from the StopInferenceExperiment API, that explains the status of the inference experiment. 
     */
    StatusReason?: InferenceExperimentStatusReason;
    /**
     * The description of the inference experiment.
     */
    Description?: InferenceExperimentDescription;
    /**
     * The timestamp at which you created the inference experiment.
     */
    CreationTime?: Timestamp;
    /**
     *  The timestamp at which the inference experiment was completed. 
     */
    CompletionTime?: Timestamp;
    /**
     * The timestamp at which you last modified the inference experiment.
     */
    LastModifiedTime?: Timestamp;
    /**
     *  The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. 
     */
    RoleArn?: RoleArn;
    /**
     * The metadata of the endpoint on which the inference experiment ran.
     */
    EndpointMetadata: EndpointMetadata;
    /**
     *  An array of ModelVariantConfigSummary objects. There is one for each variant in the inference experiment. Each ModelVariantConfigSummary object in the array describes the infrastructure configuration for deploying the corresponding variant. 
     */
    ModelVariants: ModelVariantConfigSummaryList;
    /**
     * The Amazon S3 location and configuration for storing inference request and response data.
     */
    DataStorageConfig?: InferenceExperimentDataStorageConfig;
    /**
     *  The configuration of ShadowMode inference experiment type, which shows the production variant that takes all the inference requests, and the shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant it also shows the percentage of requests that Amazon SageMaker replicates. 
     */
    ShadowModeConfig?: ShadowModeConfig;
    /**
     *  The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. For more information, see CreateInferenceExperiment. 
     */
    KmsKey?: KmsKeyId;
  }
  export interface DescribeInferenceRecommendationsJobRequest {
    /**
     * The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobName: RecommendationJobName;
  }
  export interface DescribeInferenceRecommendationsJobResponse {
    /**
     * The name of the job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobName: RecommendationJobName;
    /**
     * The job description that you provided when you initiated the job.
     */
    JobDescription?: RecommendationJobDescription;
    /**
     * The job type that you provided when you initiated the job.
     */
    JobType: RecommendationJobType;
    /**
     * The Amazon Resource Name (ARN) of the job.
     */
    JobArn: RecommendationJobArn;
    /**
     * The Amazon Resource Name (ARN) of the Amazon Web Services Identity and Access Management (IAM) role you provided when you initiated the job.
     */
    RoleArn: RoleArn;
    /**
     * The status of the job.
     */
    Status: RecommendationJobStatus;
    /**
     * A timestamp that shows when the job was created.
     */
    CreationTime: CreationTime;
    /**
     * A timestamp that shows when the job completed.
     */
    CompletionTime?: Timestamp;
    /**
     * A timestamp that shows when the job was last modified.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * If the job fails, provides information why the job failed.
     */
    FailureReason?: FailureReason;
    /**
     * Returns information about the versioned model package Amazon Resource Name (ARN), the traffic pattern, and endpoint configurations you provided when you initiated the job.
     */
    InputConfig: RecommendationJobInputConfig;
    /**
     * The stopping conditions that you provided when you initiated the job.
     */
    StoppingConditions?: RecommendationJobStoppingConditions;
    /**
     * The recommendations made by Inference Recommender.
     */
    InferenceRecommendations?: InferenceRecommendations;
    /**
     * The performance results from running an Inference Recommender job on an existing endpoint.
     */
    EndpointPerformances?: EndpointPerformances;
  }
  export interface DescribeLabelingJobRequest {
    /**
     * The name of the labeling job to return information for.
     */
    LabelingJobName: LabelingJobName;
  }
  export interface DescribeLabelingJobResponse {
    /**
     * The processing status of the labeling job. 
     */
    LabelingJobStatus: LabelingJobStatus;
    /**
     * Provides a breakdown of the number of data objects labeled by humans, the number of objects labeled by machine, the number of objects than couldn't be labeled, and the total number of objects labeled. 
     */
    LabelCounters: LabelCounters;
    /**
     * If the job failed, the reason that it failed. 
     */
    FailureReason?: FailureReason;
    /**
     * The date and time that the labeling job was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the labeling job was last updated.
     */
    LastModifiedTime: Timestamp;
    /**
     * A unique identifier for work done as part of a labeling job.
     */
    JobReferenceCode: JobReferenceCode;
    /**
     * The name assigned to the labeling job when it was created.
     */
    LabelingJobName: LabelingJobName;
    /**
     * The Amazon Resource Name (ARN) of the labeling job.
     */
    LabelingJobArn: LabelingJobArn;
    /**
     * The attribute used as the label in the output manifest file.
     */
    LabelAttributeName?: LabelAttributeName;
    /**
     * Input configuration information for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.
     */
    InputConfig: LabelingJobInputConfig;
    /**
     * The location of the job's output data and the Amazon Web Services Key Management Service key ID for the key used to encrypt the output data, if any.
     */
    OutputConfig: LabelingJobOutputConfig;
    /**
     * The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during data labeling.
     */
    RoleArn: RoleArn;
    /**
     * The S3 location of the JSON file that defines the categories used to label data objects. Please note the following label-category limits:   Semantic segmentation labeling jobs using automated labeling: 20 labels   Box bounding labeling jobs (all): 10 labels   The file is a JSON structure in the following format:  {    "document-version": "2018-11-28"    "labels": [    {    "label": "label 1"    },    {    "label": "label 2"    },    ...    {    "label": "label n"    }    ]   } 
     */
    LabelCategoryConfigS3Uri?: S3Uri;
    /**
     * A set of conditions for stopping a labeling job. If any of the conditions are met, the job is automatically stopped.
     */
    StoppingConditions?: LabelingJobStoppingConditions;
    /**
     * Configuration information for automated data labeling.
     */
    LabelingJobAlgorithmsConfig?: LabelingJobAlgorithmsConfig;
    /**
     * Configuration information required for human workers to complete a labeling task.
     */
    HumanTaskConfig: HumanTaskConfig;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * The location of the output produced by the labeling job.
     */
    LabelingJobOutput?: LabelingJobOutput;
  }
  export interface DescribeLineageGroupRequest {
    /**
     * The name of the lineage group.
     */
    LineageGroupName: ExperimentEntityName;
  }
  export interface DescribeLineageGroupResponse {
    /**
     * The name of the lineage group.
     */
    LineageGroupName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
    /**
     * The display name of the lineage group.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The description of the lineage group.
     */
    Description?: ExperimentDescription;
    /**
     * The creation time of lineage group.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * The last modified time of the lineage group.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
  }
  export interface DescribeMlflowTrackingServerRequest {
    /**
     * The name of the MLflow Tracking Server to describe.
     */
    TrackingServerName: TrackingServerName;
  }
  export interface DescribeMlflowTrackingServerResponse {
    /**
     * The ARN of the described tracking server.
     */
    TrackingServerArn?: TrackingServerArn;
    /**
     * The name of the described tracking server.
     */
    TrackingServerName?: TrackingServerName;
    /**
     * The S3 URI of the general purpose bucket used as the MLflow Tracking Server artifact store.
     */
    ArtifactStoreUri?: S3Uri;
    /**
     * The size of the described tracking server.
     */
    TrackingServerSize?: TrackingServerSize;
    /**
     * The MLflow version used for the described tracking server.
     */
    MlflowVersion?: MlflowVersion;
    /**
     * The Amazon Resource Name (ARN) for an IAM role in your account that the described MLflow Tracking Server uses to access the artifact store in Amazon S3.
     */
    RoleArn?: RoleArn;
    /**
     * The current creation status of the described MLflow Tracking Server.
     */
    TrackingServerStatus?: TrackingServerStatus;
    /**
     * Whether the described MLflow Tracking Server is currently active.
     */
    IsActive?: IsTrackingServerActive;
    /**
     * The URL to connect to the MLflow user interface for the described tracking server.
     */
    TrackingServerUrl?: TrackingServerUrl;
    /**
     * The day and time of the week when weekly maintenance occurs on the described tracking server.
     */
    WeeklyMaintenanceWindowStart?: WeeklyMaintenanceWindowStart;
    /**
     * Whether automatic registration of new MLflow models to the SageMaker Model Registry is enabled.
     */
    AutomaticModelRegistration?: Boolean;
    /**
     * The timestamp of when the described MLflow Tracking Server was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * The timestamp of when the described MLflow Tracking Server was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
  }
  export interface DescribeModelBiasJobDefinitionRequest {
    /**
     * The name of the model bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DescribeModelBiasJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model bias job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
    /**
     * The name of the bias job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The time at which the model bias job was created.
     */
    CreationTime: Timestamp;
    /**
     * The baseline configuration for a model bias job.
     */
    ModelBiasBaselineConfig?: ModelBiasBaselineConfig;
    /**
     * Configures the model bias job to run a specified Docker container image.
     */
    ModelBiasAppSpecification: ModelBiasAppSpecification;
    /**
     * Inputs for the model bias job.
     */
    ModelBiasJobInput: ModelBiasJobInput;
    ModelBiasJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Networking options for a model bias job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
  }
  export interface DescribeModelCardExportJobRequest {
    /**
     * The Amazon Resource Name (ARN) of the model card export job to describe.
     */
    ModelCardExportJobArn: ModelCardExportJobArn;
  }
  export interface DescribeModelCardExportJobResponse {
    /**
     * The name of the model card export job to describe.
     */
    ModelCardExportJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model card export job.
     */
    ModelCardExportJobArn: ModelCardExportJobArn;
    /**
     * The completion status of the model card export job.    InProgress: The model card export job is in progress.    Completed: The model card export job is complete.    Failed: The model card export job failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeModelCardExportJob call.  
     */
    Status: ModelCardExportJobStatus;
    /**
     * The name or Amazon Resource Name (ARN) of the model card that the model export job exports.
     */
    ModelCardName: EntityName;
    /**
     * The version of the model card that the model export job exports.
     */
    ModelCardVersion: Integer;
    /**
     * The export output details for the model card.
     */
    OutputConfig: ModelCardExportOutputConfig;
    /**
     * The date and time that the model export job was created.
     */
    CreatedAt: Timestamp;
    /**
     * The date and time that the model export job was last modified.
     */
    LastModifiedAt: Timestamp;
    /**
     * The failure reason if the model export job fails.
     */
    FailureReason?: FailureReason;
    /**
     * The exported model card artifacts.
     */
    ExportArtifacts?: ModelCardExportArtifacts;
  }
  export interface DescribeModelCardRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the model card to describe.
     */
    ModelCardName: ModelCardNameOrArn;
    /**
     * The version of the model card to describe. If a version is not provided, then the latest version of the model card is described.
     */
    ModelCardVersion?: Integer;
  }
  export interface DescribeModelCardResponse {
    /**
     * The Amazon Resource Name (ARN) of the model card.
     */
    ModelCardArn: ModelCardArn;
    /**
     * The name of the model card.
     */
    ModelCardName: EntityName;
    /**
     * The version of the model card.
     */
    ModelCardVersion: Integer;
    /**
     * The content of the model card.
     */
    Content: ModelCardContent;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus: ModelCardStatus;
    /**
     * The security configuration used to protect model card content.
     */
    SecurityConfig?: ModelCardSecurityConfig;
    /**
     * The date and time the model card was created.
     */
    CreationTime: Timestamp;
    CreatedBy: UserContext;
    /**
     * The date and time the model card was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * The processing status of model card deletion. The ModelCardProcessingStatus updates throughout the different deletion steps.    DeletePending: Model card deletion request received.    DeleteInProgress: Model card deletion is in progress.    ContentDeleted: Deleted model card content.    ExportJobsDeleted: Deleted all export jobs associated with the model card.    DeleteCompleted: Successfully deleted the model card.    DeleteFailed: The model card failed to delete.  
     */
    ModelCardProcessingStatus?: ModelCardProcessingStatus;
  }
  export interface DescribeModelExplainabilityJobDefinitionRequest {
    /**
     * The name of the model explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DescribeModelExplainabilityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model explainability job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
    /**
     * The name of the explainability job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The time at which the model explainability job was created.
     */
    CreationTime: Timestamp;
    /**
     * The baseline configuration for a model explainability job.
     */
    ModelExplainabilityBaselineConfig?: ModelExplainabilityBaselineConfig;
    /**
     * Configures the model explainability job to run a specified Docker container image.
     */
    ModelExplainabilityAppSpecification: ModelExplainabilityAppSpecification;
    /**
     * Inputs for the model explainability job.
     */
    ModelExplainabilityJobInput: ModelExplainabilityJobInput;
    ModelExplainabilityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Networking options for a model explainability job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that has read permission to the input data location and write permission to the output data location in Amazon S3.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
  }
  export interface DescribeModelInput {
    /**
     * The name of the model.
     */
    ModelName: ModelName;
  }
  export interface DescribeModelOutput {
    /**
     * Name of the SageMaker model.
     */
    ModelName: ModelName;
    /**
     * The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production. 
     */
    PrimaryContainer?: ContainerDefinition;
    /**
     * The containers in the inference pipeline.
     */
    Containers?: ContainerDefinitionList;
    /**
     * Specifies details of how containers in a multi-container endpoint are called.
     */
    InferenceExecutionConfig?: InferenceExecutionConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
     */
    ExecutionRoleArn?: RoleArn;
    /**
     * A VpcConfig object that specifies the VPC that this model has access to. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud 
     */
    VpcConfig?: VpcConfig;
    /**
     * A timestamp that shows when the model was created.
     */
    CreationTime: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of the model.
     */
    ModelArn: ModelArn;
    /**
     * If True, no inbound or outbound network calls can be made to or from the model container.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * A set of recommended deployment configurations for the model.
     */
    DeploymentRecommendation?: DeploymentRecommendation;
  }
  export interface DescribeModelPackageGroupInput {
    /**
     * The name of the model group to describe.
     */
    ModelPackageGroupName: ArnOrName;
  }
  export interface DescribeModelPackageGroupOutput {
    /**
     * The name of the model group.
     */
    ModelPackageGroupName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model group.
     */
    ModelPackageGroupArn: ModelPackageGroupArn;
    /**
     * A description of the model group.
     */
    ModelPackageGroupDescription?: EntityDescription;
    /**
     * The time that the model group was created.
     */
    CreationTime: CreationTime;
    CreatedBy: UserContext;
    /**
     * The status of the model group.
     */
    ModelPackageGroupStatus: ModelPackageGroupStatus;
  }
  export interface DescribeModelPackageInput {
    /**
     * The name or Amazon Resource Name (ARN) of the model package to describe. When you specify a name, the name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).
     */
    ModelPackageName: VersionedArnOrName;
  }
  export interface DescribeModelPackageOutput {
    /**
     * The name of the model package being described.
     */
    ModelPackageName: EntityName;
    /**
     * If the model is a versioned model, the name of the model group that the versioned model belongs to.
     */
    ModelPackageGroupName?: EntityName;
    /**
     * The version of the model package.
     */
    ModelPackageVersion?: ModelPackageVersion;
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    ModelPackageArn: ModelPackageArn;
    /**
     * A brief summary of the model package.
     */
    ModelPackageDescription?: EntityDescription;
    /**
     * A timestamp specifying when the model package was created.
     */
    CreationTime: CreationTime;
    /**
     * Details about inference jobs that you can run with models based on this model package.
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * Details about the algorithm that was used to create the model package.
     */
    SourceAlgorithmSpecification?: SourceAlgorithmSpecification;
    /**
     * Configurations for one or more transform jobs that SageMaker runs to test the model package.
     */
    ValidationSpecification?: ModelPackageValidationSpecification;
    /**
     * The current status of the model package.
     */
    ModelPackageStatus: ModelPackageStatus;
    /**
     * Details about the current status of the model package.
     */
    ModelPackageStatusDetails: ModelPackageStatusDetails;
    /**
     * Whether the model package is certified for listing on Amazon Web Services Marketplace.
     */
    CertifyForMarketplace?: CertifyForMarketplace;
    /**
     * The approval status of the model package.
     */
    ModelApprovalStatus?: ModelApprovalStatus;
    CreatedBy?: UserContext;
    MetadataProperties?: MetadataProperties;
    /**
     * Metrics for the model.
     */
    ModelMetrics?: ModelMetrics;
    /**
     * The last time that the model package was modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * A description provided for the model approval.
     */
    ApprovalDescription?: ApprovalDescription;
    /**
     * The machine learning domain of the model package you specified. Common machine learning domains include computer vision and natural language processing.
     */
    Domain?: String;
    /**
     * The machine learning task you specified that your model package accomplishes. Common machine learning tasks include object detection and image classification.
     */
    Task?: String;
    /**
     * The Amazon Simple Storage Service (Amazon S3) path where the sample payload are stored. This path points to a single gzip compressed tar archive (.tar.gz suffix).
     */
    SamplePayloadUrl?: String;
    /**
     * The metadata properties associated with the model package versions.
     */
    CustomerMetadataProperties?: CustomerMetadataMap;
    /**
     * Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide. 
     */
    DriftCheckBaselines?: DriftCheckBaselines;
    /**
     * An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
     */
    AdditionalInferenceSpecifications?: AdditionalInferenceSpecifications;
    /**
     * Indicates if you want to skip model validation.
     */
    SkipModelValidation?: SkipModelValidation;
    /**
     * The URI of the source for the model package.
     */
    SourceUri?: ModelPackageSourceUri;
    /**
     * The KMS Key ID (KMSKeyId) used for encryption of model package information.
     */
    SecurityConfig?: ModelPackageSecurityConfig;
    /**
     * The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
     */
    ModelCard?: ModelPackageModelCard;
  }
  export interface DescribeModelQualityJobDefinitionRequest {
    /**
     * The name of the model quality job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
  }
  export interface DescribeModelQualityJobDefinitionResponse {
    /**
     * The Amazon Resource Name (ARN) of the model quality job.
     */
    JobDefinitionArn: MonitoringJobDefinitionArn;
    /**
     * The name of the quality job definition. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    JobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The time at which the model quality job was created.
     */
    CreationTime: Timestamp;
    /**
     * The baseline configuration for a model quality job.
     */
    ModelQualityBaselineConfig?: ModelQualityBaselineConfig;
    /**
     * Configures the model quality job to run a specified Docker container image.
     */
    ModelQualityAppSpecification: ModelQualityAppSpecification;
    /**
     * Inputs for the model quality job.
     */
    ModelQualityJobInput: ModelQualityJobInput;
    ModelQualityJobOutputConfig: MonitoringOutputConfig;
    JobResources: MonitoringResources;
    /**
     * Networking options for a model quality job.
     */
    NetworkConfig?: MonitoringNetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    StoppingCondition?: MonitoringStoppingCondition;
  }
  export interface DescribeMonitoringScheduleRequest {
    /**
     * Name of a previously created monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
  }
  export interface DescribeMonitoringScheduleResponse {
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn: MonitoringScheduleArn;
    /**
     * Name of the monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The status of an monitoring job.
     */
    MonitoringScheduleStatus: ScheduleStatus;
    /**
     * The type of the monitoring job that this schedule runs. This is one of the following values.    DATA_QUALITY - The schedule is for a data quality monitoring job.    MODEL_QUALITY - The schedule is for a model quality monitoring job.    MODEL_BIAS - The schedule is for a bias monitoring job.    MODEL_EXPLAINABILITY - The schedule is for an explainability monitoring job.  
     */
    MonitoringType?: MonitoringType;
    /**
     * A string, up to one KB in size, that contains the reason a monitoring job failed, if it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The time at which the monitoring job was created.
     */
    CreationTime: Timestamp;
    /**
     * The time at which the monitoring job was last modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The configuration object that specifies the monitoring schedule and defines the monitoring job.
     */
    MonitoringScheduleConfig: MonitoringScheduleConfig;
    /**
     *  The name of the endpoint for the monitoring job.
     */
    EndpointName?: EndpointName;
    /**
     * Describes metadata on the last execution to run, if there was one.
     */
    LastMonitoringExecutionSummary?: MonitoringExecutionSummary;
  }
  export interface DescribeNotebookInstanceInput {
    /**
     * The name of the notebook instance that you want information about.
     */
    NotebookInstanceName: NotebookInstanceName;
  }
  export interface DescribeNotebookInstanceLifecycleConfigInput {
    /**
     * The name of the lifecycle configuration to describe.
     */
    NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName;
  }
  export interface DescribeNotebookInstanceLifecycleConfigOutput {
    /**
     * The Amazon Resource Name (ARN) of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigArn?: NotebookInstanceLifecycleConfigArn;
    /**
     * The name of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigName?: NotebookInstanceLifecycleConfigName;
    /**
     * The shell script that runs only once, when you create a notebook instance.
     */
    OnCreate?: NotebookInstanceLifecycleConfigList;
    /**
     * The shell script that runs every time you start a notebook instance, including when you create the notebook instance.
     */
    OnStart?: NotebookInstanceLifecycleConfigList;
    /**
     * A timestamp that tells when the lifecycle configuration was last modified.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * A timestamp that tells when the lifecycle configuration was created.
     */
    CreationTime?: CreationTime;
  }
  export interface DescribeNotebookInstanceOutput {
    /**
     * The Amazon Resource Name (ARN) of the notebook instance.
     */
    NotebookInstanceArn?: NotebookInstanceArn;
    /**
     * The name of the SageMaker notebook instance. 
     */
    NotebookInstanceName?: NotebookInstanceName;
    /**
     * The status of the notebook instance.
     */
    NotebookInstanceStatus?: NotebookInstanceStatus;
    /**
     * If status is Failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The URL that you use to connect to the Jupyter notebook that is running in your notebook instance. 
     */
    Url?: NotebookInstanceUrl;
    /**
     * The type of ML compute instance running on the notebook instance.
     */
    InstanceType?: InstanceType;
    /**
     * The ID of the VPC subnet.
     */
    SubnetId?: SubnetId;
    /**
     * The IDs of the VPC security groups.
     */
    SecurityGroups?: SecurityGroupIds;
    /**
     * The Amazon Resource Name (ARN) of the IAM role associated with the instance. 
     */
    RoleArn?: RoleArn;
    /**
     * The Amazon Web Services KMS key ID SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance. 
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The network interface IDs that SageMaker created at the time of creating the instance. 
     */
    NetworkInterfaceId?: NetworkInterfaceId;
    /**
     * A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified. 
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * A timestamp. Use this parameter to return the time when the notebook instance was created
     */
    CreationTime?: CreationTime;
    /**
     * Returns the name of a notebook instance lifecycle configuration. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance 
     */
    NotebookInstanceLifecycleConfigName?: NotebookInstanceLifecycleConfigName;
    /**
     * Describes whether SageMaker provides internet access to the notebook instance. If this value is set to Disabled, the notebook instance does not have internet access, and cannot connect to SageMaker training and endpoint services. For more information, see Notebook Instances Are Internet-Enabled by Default.
     */
    DirectInternetAccess?: DirectInternetAccess;
    /**
     * The size, in GB, of the ML storage volume attached to the notebook instance.
     */
    VolumeSizeInGB?: NotebookInstanceVolumeSizeInGB;
    /**
     * A list of the Elastic Inference (EI) instance types associated with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
     */
    AcceleratorTypes?: NotebookInstanceAcceleratorTypes;
    /**
     * The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    DefaultCodeRepository?: CodeRepositoryNameOrUrl;
    /**
     * An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    AdditionalCodeRepositories?: AdditionalCodeRepositoryNamesOrUrls;
    /**
     * Whether root access is enabled or disabled for users of the notebook instance.  Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users. 
     */
    RootAccess?: RootAccess;
    /**
     * The platform identifier of the notebook instance runtime environment.
     */
    PlatformIdentifier?: PlatformIdentifier;
    /**
     * Information on the IMDS configuration of the notebook instance
     */
    InstanceMetadataServiceConfiguration?: InstanceMetadataServiceConfiguration;
  }
  export interface DescribeOptimizationJobRequest {
    /**
     * The name that you assigned to the optimization job.
     */
    OptimizationJobName: EntityName;
  }
  export interface DescribeOptimizationJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the optimization job.
     */
    OptimizationJobArn: OptimizationJobArn;
    /**
     * The current status of the optimization job.
     */
    OptimizationJobStatus: OptimizationJobStatus;
    /**
     * The time when the optimization job started.
     */
    OptimizationStartTime?: Timestamp;
    /**
     * The time when the optimization job finished processing.
     */
    OptimizationEndTime?: Timestamp;
    /**
     * The time when you created the optimization job.
     */
    CreationTime: CreationTime;
    /**
     * The time when the optimization job was last updated.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * If the optimization job status is FAILED, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * The name that you assigned to the optimization job.
     */
    OptimizationJobName: EntityName;
    /**
     * The location of the source model to optimize with an optimization job.
     */
    ModelSource: OptimizationJobModelSource;
    /**
     * The environment variables to set in the model container.
     */
    OptimizationEnvironment?: OptimizationJobEnvironmentVariables;
    /**
     * The type of instance that hosts the optimized model that you create with the optimization job.
     */
    DeploymentInstanceType: OptimizationJobDeploymentInstanceType;
    /**
     * Settings for each of the optimization techniques that the job applies.
     */
    OptimizationConfigs: OptimizationConfigs;
    /**
     * Details for where to store the optimized model that you create with the optimization job.
     */
    OutputConfig: OptimizationJobOutputConfig;
    /**
     * Output values produced by an optimization job.
     */
    OptimizationOutput?: OptimizationOutput;
    /**
     * The ARN of the IAM role that you assigned to the optimization job.
     */
    RoleArn: RoleArn;
    StoppingCondition: StoppingCondition;
    /**
     * A VPC in Amazon VPC that your optimized model has access to.
     */
    VpcConfig?: OptimizationVpcConfig;
  }
  export interface DescribePipelineDefinitionForExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
  }
  export interface DescribePipelineDefinitionForExecutionResponse {
    /**
     * The JSON pipeline definition.
     */
    PipelineDefinition?: PipelineDefinition;
    /**
     * The time when the pipeline was created.
     */
    CreationTime?: Timestamp;
  }
  export interface DescribePipelineExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
  }
  export interface DescribePipelineExecutionResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineArn?: PipelineArn;
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
    /**
     * The display name of the pipeline execution.
     */
    PipelineExecutionDisplayName?: PipelineExecutionName;
    /**
     * The status of the pipeline execution.
     */
    PipelineExecutionStatus?: PipelineExecutionStatus;
    /**
     * The description of the pipeline execution.
     */
    PipelineExecutionDescription?: PipelineExecutionDescription;
    PipelineExperimentConfig?: PipelineExperimentConfig;
    /**
     * If the execution failed, a message describing why.
     */
    FailureReason?: PipelineExecutionFailureReason;
    /**
     * The time when the pipeline execution was created.
     */
    CreationTime?: Timestamp;
    /**
     * The time when the pipeline execution was modified last.
     */
    LastModifiedTime?: Timestamp;
    CreatedBy?: UserContext;
    LastModifiedBy?: UserContext;
    /**
     * The parallelism configuration applied to the pipeline.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
    /**
     * The selective execution configuration applied to the pipeline run.
     */
    SelectiveExecutionConfig?: SelectiveExecutionConfig;
  }
  export interface DescribePipelineRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the pipeline to describe.
     */
    PipelineName: PipelineNameOrArn;
  }
  export interface DescribePipelineResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineArn?: PipelineArn;
    /**
     * The name of the pipeline.
     */
    PipelineName?: PipelineName;
    /**
     * The display name of the pipeline.
     */
    PipelineDisplayName?: PipelineName;
    /**
     * The JSON pipeline definition.
     */
    PipelineDefinition?: PipelineDefinition;
    /**
     * The description of the pipeline.
     */
    PipelineDescription?: PipelineDescription;
    /**
     * The Amazon Resource Name (ARN) that the pipeline uses to execute.
     */
    RoleArn?: RoleArn;
    /**
     * The status of the pipeline execution.
     */
    PipelineStatus?: PipelineStatus;
    /**
     * The time when the pipeline was created.
     */
    CreationTime?: Timestamp;
    /**
     * The time when the pipeline was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The time when the pipeline was last run.
     */
    LastRunTime?: Timestamp;
    CreatedBy?: UserContext;
    LastModifiedBy?: UserContext;
    /**
     * Lists the parallelism configuration applied to the pipeline.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
  }
  export interface DescribeProcessingJobRequest {
    /**
     * The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    ProcessingJobName: ProcessingJobName;
  }
  export interface DescribeProcessingJobResponse {
    /**
     * The inputs for a processing job.
     */
    ProcessingInputs?: ProcessingInputs;
    /**
     * Output configuration for the processing job.
     */
    ProcessingOutputConfig?: ProcessingOutputConfig;
    /**
     * The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.
     */
    ProcessingJobName: ProcessingJobName;
    /**
     * Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.
     */
    ProcessingResources: ProcessingResources;
    /**
     * The time limit for how long the processing job is allowed to run.
     */
    StoppingCondition?: ProcessingStoppingCondition;
    /**
     * Configures the processing job to run a specified container image.
     */
    AppSpecification: AppSpecification;
    /**
     * The environment variables set in the Docker container.
     */
    Environment?: ProcessingEnvironmentMap;
    /**
     * Networking options for a processing job.
     */
    NetworkConfig?: NetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn?: RoleArn;
    /**
     * The configuration information used to create an experiment.
     */
    ExperimentConfig?: ExperimentConfig;
    /**
     * The Amazon Resource Name (ARN) of the processing job.
     */
    ProcessingJobArn: ProcessingJobArn;
    /**
     * Provides the status of a processing job.
     */
    ProcessingJobStatus: ProcessingJobStatus;
    /**
     * An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
     */
    ExitMessage?: ExitMessage;
    /**
     * A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The time at which the processing job completed.
     */
    ProcessingEndTime?: Timestamp;
    /**
     * The time at which the processing job started.
     */
    ProcessingStartTime?: Timestamp;
    /**
     * The time at which the processing job was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The time at which the processing job was created.
     */
    CreationTime: Timestamp;
    /**
     * The ARN of a monitoring schedule for an endpoint associated with this processing job.
     */
    MonitoringScheduleArn?: MonitoringScheduleArn;
    /**
     * The ARN of an AutoML job associated with this processing job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    /**
     * The ARN of a training job associated with this processing job.
     */
    TrainingJobArn?: TrainingJobArn;
  }
  export interface DescribeProjectInput {
    /**
     * The name of the project to describe.
     */
    ProjectName: ProjectEntityName;
  }
  export interface DescribeProjectOutput {
    /**
     * The Amazon Resource Name (ARN) of the project.
     */
    ProjectArn: ProjectArn;
    /**
     * The name of the project.
     */
    ProjectName: ProjectEntityName;
    /**
     * The ID of the project.
     */
    ProjectId: ProjectId;
    /**
     * The description of the project.
     */
    ProjectDescription?: EntityDescription;
    /**
     * Information used to provision a service catalog product. For information, see What is Amazon Web Services Service Catalog.
     */
    ServiceCatalogProvisioningDetails: ServiceCatalogProvisioningDetails;
    /**
     * Information about a provisioned service catalog product.
     */
    ServiceCatalogProvisionedProductDetails?: ServiceCatalogProvisionedProductDetails;
    /**
     * The status of the project.
     */
    ProjectStatus: ProjectStatus;
    CreatedBy?: UserContext;
    /**
     * The time when the project was created.
     */
    CreationTime: Timestamp;
    /**
     * The timestamp when project was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
  }
  export interface DescribeSpaceRequest {
    /**
     * The ID of the associated domain.
     */
    DomainId: DomainId;
    /**
     * The name of the space.
     */
    SpaceName: SpaceName;
  }
  export interface DescribeSpaceResponse {
    /**
     * The ID of the associated domain.
     */
    DomainId?: DomainId;
    /**
     * The space's Amazon Resource Name (ARN).
     */
    SpaceArn?: SpaceArn;
    /**
     * The name of the space.
     */
    SpaceName?: SpaceName;
    /**
     * The ID of the space's profile in the Amazon EFS volume.
     */
    HomeEfsFileSystemUid?: EfsUid;
    /**
     * The status.
     */
    Status?: SpaceStatus;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The failure reason.
     */
    FailureReason?: FailureReason;
    /**
     * A collection of space settings.
     */
    SpaceSettings?: SpaceSettings;
    /**
     * The collection of ownership settings for a space.
     */
    OwnershipSettings?: OwnershipSettings;
    /**
     * The collection of space sharing settings for a space.
     */
    SpaceSharingSettings?: SpaceSharingSettings;
    /**
     * The name of the space that appears in the Amazon SageMaker Studio UI.
     */
    SpaceDisplayName?: NonEmptyString64;
    /**
     * Returns the URL of the space. If the space is created with Amazon Web Services IAM Identity Center (Successor to Amazon Web Services Single Sign-On) authentication, users can navigate to the URL after appending the respective redirect parameter for the application type to be federated through Amazon Web Services IAM Identity Center. The following application types are supported:   Studio Classic: &amp;redirect=JupyterServer    JupyterLab: &amp;redirect=JupyterLab    Code Editor, based on Code-OSS, Visual Studio Code - Open Source: &amp;redirect=CodeEditor   
     */
    Url?: String1024;
  }
  export interface DescribeStudioLifecycleConfigRequest {
    /**
     * The name of the Amazon SageMaker Studio Lifecycle Configuration to describe.
     */
    StudioLifecycleConfigName: StudioLifecycleConfigName;
  }
  export interface DescribeStudioLifecycleConfigResponse {
    /**
     * The ARN of the Lifecycle Configuration to describe.
     */
    StudioLifecycleConfigArn?: StudioLifecycleConfigArn;
    /**
     * The name of the Amazon SageMaker Studio Lifecycle Configuration that is described.
     */
    StudioLifecycleConfigName?: StudioLifecycleConfigName;
    /**
     * The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
     */
    CreationTime?: Timestamp;
    /**
     * This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The content of your Amazon SageMaker Studio Lifecycle Configuration script.
     */
    StudioLifecycleConfigContent?: StudioLifecycleConfigContent;
    /**
     * The App type that the Lifecycle Configuration is attached to.
     */
    StudioLifecycleConfigAppType?: StudioLifecycleConfigAppType;
  }
  export interface DescribeSubscribedWorkteamRequest {
    /**
     * The Amazon Resource Name (ARN) of the subscribed work team to describe.
     */
    WorkteamArn: WorkteamArn;
  }
  export interface DescribeSubscribedWorkteamResponse {
    /**
     * A Workteam instance that contains information about the work team.
     */
    SubscribedWorkteam: SubscribedWorkteam;
  }
  export interface DescribeTrainingJobRequest {
    /**
     * The name of the training job.
     */
    TrainingJobName: TrainingJobName;
  }
  export interface DescribeTrainingJobResponse {
    /**
     *  Name of the model training job. 
     */
    TrainingJobName: TrainingJobName;
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn: TrainingJobArn;
    /**
     * The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
     */
    TuningJobArn?: HyperParameterTuningJobArn;
    /**
     * The Amazon Resource Name (ARN) of the SageMaker Ground Truth labeling job that created the transform or training job.
     */
    LabelingJobArn?: LabelingJobArn;
    /**
     * The Amazon Resource Name (ARN) of an AutoML job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    /**
     * Information about the Amazon S3 location that is configured for storing model artifacts. 
     */
    ModelArtifacts: ModelArtifacts;
    /**
     * The status of the training job. SageMaker provides the following training job statuses:    InProgress - The training is in progress.    Completed - The training job has completed.    Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.    Stopping - The training job is stopping.    Stopped - The training job has stopped.   For more detailed information, see SecondaryStatus. 
     */
    TrainingJobStatus: TrainingJobStatus;
    /**
     *  Provides detailed information about the state of the training job. For detailed information on the secondary status of the training job, see StatusMessage under SecondaryStatusTransition. SageMaker provides primary statuses and secondary statuses that apply to each of them:  InProgress     Starting - Starting the training job.    Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.    Training - Training is in progress.    Interrupted - The job stopped because the managed spot training instances were interrupted.     Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.    Completed     Completed - The training job has completed.    Failed     Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.    Stopped     MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.    MaxWaitTimeExceeded - The job stopped because it exceeded the maximum allowed wait time.    Stopped - The training job has stopped.    Stopping     Stopping - Stopping the training job.      Valid values for SecondaryStatus are subject to change.   We no longer support the following secondary statuses:    LaunchingMLInstances     PreparingTraining     DownloadingTrainingImage   
     */
    SecondaryStatus: SecondaryStatus;
    /**
     * If the training job failed, the reason it failed. 
     */
    FailureReason?: FailureReason;
    /**
     * Algorithm-specific parameters. 
     */
    HyperParameters?: HyperParameters;
    /**
     * Information about the algorithm used for training, and algorithm metadata. 
     */
    AlgorithmSpecification: AlgorithmSpecification;
    /**
     * The Amazon Web Services Identity and Access Management (IAM) role configured for the training job. 
     */
    RoleArn?: RoleArn;
    /**
     * An array of Channel objects that describes each data input channel. 
     */
    InputDataConfig?: InputDataConfig;
    /**
     * The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts. 
     */
    OutputDataConfig?: OutputDataConfig;
    /**
     * Resources, including ML compute instances and ML storage volumes, that are configured for model training. 
     */
    ResourceConfig: ResourceConfig;
    /**
     * The status of the warm pool associated with the training job.
     */
    WarmPoolStatus?: WarmPoolStatus;
    /**
     * A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: VpcConfig;
    /**
     * Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. 
     */
    StoppingCondition: StoppingCondition;
    /**
     * A timestamp that indicates when the training job was created.
     */
    CreationTime: Timestamp;
    /**
     * Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
     */
    TrainingStartTime?: Timestamp;
    /**
     * Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
     */
    TrainingEndTime?: Timestamp;
    /**
     * A timestamp that indicates when the status of the training job was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * A history of all of the secondary statuses that the training job has transitioned through.
     */
    SecondaryStatusTransitions?: SecondaryStatusTransitions;
    /**
     * A collection of MetricData objects that specify the names, values, and dates and times that the training algorithm emitted to Amazon CloudWatch.
     */
    FinalMetricDataList?: FinalMetricDataList;
    /**
     * If you want to allow inbound or outbound network calls, except for calls between peers within a training cluster for distributed training, choose True. If you enable network isolation for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithms in distributed training.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * A Boolean indicating whether managed spot training is enabled (True) or not (False).
     */
    EnableManagedSpotTraining?: Boolean;
    CheckpointConfig?: CheckpointConfig;
    /**
     * The training time in seconds.
     */
    TrainingTimeInSeconds?: TrainingTimeInSeconds;
    /**
     * The billable time in seconds. Billable time refers to the absolute wall-clock time. Multiply BillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run distributed training. The formula is as follows: BillableTimeInSeconds * InstanceCount . You can calculate the savings from using managed spot training using the formula (1 - BillableTimeInSeconds / TrainingTimeInSeconds) * 100. For example, if BillableTimeInSeconds is 100 and TrainingTimeInSeconds is 500, the savings is 80%.
     */
    BillableTimeInSeconds?: BillableTimeInSeconds;
    DebugHookConfig?: DebugHookConfig;
    ExperimentConfig?: ExperimentConfig;
    /**
     * Configuration information for Amazon SageMaker Debugger rules for debugging output tensors.
     */
    DebugRuleConfigurations?: DebugRuleConfigurations;
    TensorBoardOutputConfig?: TensorBoardOutputConfig;
    /**
     * Evaluation status of Amazon SageMaker Debugger rules for debugging on a training job.
     */
    DebugRuleEvaluationStatuses?: DebugRuleEvaluationStatuses;
    ProfilerConfig?: ProfilerConfig;
    /**
     * Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
     */
    ProfilerRuleConfigurations?: ProfilerRuleConfigurations;
    /**
     * Evaluation status of Amazon SageMaker Debugger rules for profiling on a training job.
     */
    ProfilerRuleEvaluationStatuses?: ProfilerRuleEvaluationStatuses;
    /**
     * Profiling status of a training job.
     */
    ProfilingStatus?: ProfilingStatus;
    /**
     * The environment variables to set in the Docker container.
     */
    Environment?: TrainingEnvironmentMap;
    /**
     * The number of times to retry the job when the job fails due to an InternalServerError.
     */
    RetryStrategy?: RetryStrategy;
    /**
     * Configuration for remote debugging. To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
     */
    RemoteDebugConfig?: RemoteDebugConfig;
    /**
     * Contains information about the infrastructure health check configuration for the training job.
     */
    InfraCheckConfig?: InfraCheckConfig;
  }
  export interface DescribeTransformJobRequest {
    /**
     * The name of the transform job that you want to view details of.
     */
    TransformJobName: TransformJobName;
  }
  export interface DescribeTransformJobResponse {
    /**
     * The name of the transform job.
     */
    TransformJobName: TransformJobName;
    /**
     * The Amazon Resource Name (ARN) of the transform job.
     */
    TransformJobArn: TransformJobArn;
    /**
     * The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.
     */
    TransformJobStatus: TransformJobStatus;
    /**
     * If the transform job failed, FailureReason describes why it failed. A transform job creates a log file, which includes error messages, and stores it as an Amazon S3 object. For more information, see Log Amazon SageMaker Events with Amazon CloudWatch.
     */
    FailureReason?: FailureReason;
    /**
     * The name of the model used in the transform job.
     */
    ModelName: ModelName;
    /**
     * The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.
     */
    MaxConcurrentTransforms?: MaxConcurrentTransforms;
    /**
     * The timeout and maximum number of retries for processing a transform job invocation.
     */
    ModelClientConfig?: ModelClientConfig;
    /**
     * The maximum payload size, in MB, used in the transform job.
     */
    MaxPayloadInMB?: MaxPayloadInMB;
    /**
     * Specifies the number of records to include in a mini-batch for an HTTP inference request. A record  is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.  To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.
     */
    BatchStrategy?: BatchStrategy;
    /**
     * The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
     */
    Environment?: TransformEnvironmentMap;
    /**
     * Describes the dataset to be transformed and the Amazon S3 location where it is stored.
     */
    TransformInput: TransformInput;
    /**
     * Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
     */
    TransformOutput?: TransformOutput;
    /**
     * Configuration to control how SageMaker captures inference data.
     */
    DataCaptureConfig?: BatchDataCaptureConfig;
    /**
     * Describes the resources, including ML instance types and ML instance count, to use for the transform job.
     */
    TransformResources: TransformResources;
    /**
     * A timestamp that shows when the transform Job was created.
     */
    CreationTime: Timestamp;
    /**
     * Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.
     */
    TransformStartTime?: Timestamp;
    /**
     * Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.
     */
    TransformEndTime?: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of the Amazon SageMaker Ground Truth labeling job that created the transform or training job.
     */
    LabelingJobArn?: LabelingJobArn;
    /**
     * The Amazon Resource Name (ARN) of the AutoML transform job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    DataProcessing?: DataProcessing;
    ExperimentConfig?: ExperimentConfig;
  }
  export interface DescribeTrialComponentRequest {
    /**
     * The name of the trial component to describe.
     */
    TrialComponentName: ExperimentEntityNameOrArn;
  }
  export interface DescribeTrialComponentResponse {
    /**
     * The name of the trial component.
     */
    TrialComponentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    /**
     * The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the source and, optionally, the job type.
     */
    Source?: TrialComponentSource;
    /**
     * The status of the component. States include:   InProgress   Completed   Failed  
     */
    Status?: TrialComponentStatus;
    /**
     * When the component started.
     */
    StartTime?: Timestamp;
    /**
     * When the component ended.
     */
    EndTime?: Timestamp;
    /**
     * When the component was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the trial component.
     */
    CreatedBy?: UserContext;
    /**
     * When the component was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * Who last modified the component.
     */
    LastModifiedBy?: UserContext;
    /**
     * The hyperparameters of the component.
     */
    Parameters?: TrialComponentParameters;
    /**
     * The input artifacts of the component.
     */
    InputArtifacts?: TrialComponentArtifacts;
    /**
     * The output artifacts of the component.
     */
    OutputArtifacts?: TrialComponentArtifacts;
    MetadataProperties?: MetadataProperties;
    /**
     * The metrics for the component.
     */
    Metrics?: TrialComponentMetricSummaries;
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
    /**
     * A list of ARNs and, if applicable, job types for multiple sources of an experiment run.
     */
    Sources?: TrialComponentSources;
  }
  export interface DescribeTrialRequest {
    /**
     * The name of the trial to describe.
     */
    TrialName: ExperimentEntityName;
  }
  export interface DescribeTrialResponse {
    /**
     * The name of the trial.
     */
    TrialName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
    /**
     * The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The name of the experiment the trial is part of.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the source and, optionally, the job type.
     */
    Source?: TrialSource;
    /**
     * When the trial was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the trial.
     */
    CreatedBy?: UserContext;
    /**
     * When the trial was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * Who last modified the trial.
     */
    LastModifiedBy?: UserContext;
    MetadataProperties?: MetadataProperties;
  }
  export interface DescribeUserProfileRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name. This value is not case sensitive.
     */
    UserProfileName: UserProfileName;
  }
  export interface DescribeUserProfileResponse {
    /**
     * The ID of the domain that contains the profile.
     */
    DomainId?: DomainId;
    /**
     * The user profile Amazon Resource Name (ARN).
     */
    UserProfileArn?: UserProfileArn;
    /**
     * The user profile name.
     */
    UserProfileName?: UserProfileName;
    /**
     * The ID of the user's profile in the Amazon Elastic File System volume.
     */
    HomeEfsFileSystemUid?: EfsUid;
    /**
     * The status.
     */
    Status?: UserProfileStatus;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The failure reason.
     */
    FailureReason?: FailureReason;
    /**
     * The IAM Identity Center user identifier.
     */
    SingleSignOnUserIdentifier?: SingleSignOnUserIdentifier;
    /**
     * The IAM Identity Center user value.
     */
    SingleSignOnUserValue?: String256;
    /**
     * A collection of settings.
     */
    UserSettings?: UserSettings;
  }
  export interface DescribeWorkforceRequest {
    /**
     * The name of the private workforce whose access you want to restrict. WorkforceName is automatically set to default when a workforce is created and cannot be modified. 
     */
    WorkforceName: WorkforceName;
  }
  export interface DescribeWorkforceResponse {
    /**
     * A single private workforce, which is automatically created when you create your first private work team. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
     */
    Workforce: Workforce;
  }
  export interface DescribeWorkteamRequest {
    /**
     * The name of the work team to return a description of.
     */
    WorkteamName: WorkteamName;
  }
  export interface DescribeWorkteamResponse {
    /**
     * A Workteam instance that contains information about the work team. 
     */
    Workteam: Workteam;
  }
  export type Description = string;
  export interface DesiredWeightAndCapacity {
    /**
     * The name of the variant to update.
     */
    VariantName: VariantName;
    /**
     * The variant's weight.
     */
    DesiredWeight?: VariantWeight;
    /**
     * The variant's capacity.
     */
    DesiredInstanceCount?: TaskCount;
    /**
     * Specifies the serverless update concurrency configuration for an endpoint variant.
     */
    ServerlessUpdateConfig?: ProductionVariantServerlessUpdateConfig;
  }
  export type DesiredWeightAndCapacityList = DesiredWeightAndCapacity[];
  export type DestinationS3Uri = string;
  export type DetailedAlgorithmStatus = "NotStarted"|"InProgress"|"Completed"|"Failed"|string;
  export type DetailedModelPackageStatus = "NotStarted"|"InProgress"|"Completed"|"Failed"|string;
  export interface Device {
    /**
     * The name of the device.
     */
    DeviceName: DeviceName;
    /**
     * Description of the device.
     */
    Description?: DeviceDescription;
    /**
     * Amazon Web Services Internet of Things (IoT) object name.
     */
    IotThingName?: ThingName;
  }
  export type DeviceArn = string;
  export type DeviceDeploymentStatus = "READYTODEPLOY"|"INPROGRESS"|"DEPLOYED"|"FAILED"|"STOPPING"|"STOPPED"|string;
  export type DeviceDeploymentSummaries = DeviceDeploymentSummary[];
  export interface DeviceDeploymentSummary {
    /**
     * The ARN of the edge deployment plan.
     */
    EdgeDeploymentPlanArn: EdgeDeploymentPlanArn;
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * The name of the stage in the edge deployment plan.
     */
    StageName: EntityName;
    /**
     * The name of the deployed stage.
     */
    DeployedStageName?: EntityName;
    /**
     * The name of the fleet to which the device belongs to.
     */
    DeviceFleetName?: EntityName;
    /**
     * The name of the device.
     */
    DeviceName: DeviceName;
    /**
     * The ARN of the device.
     */
    DeviceArn: DeviceArn;
    /**
     * The deployment status of the device.
     */
    DeviceDeploymentStatus?: DeviceDeploymentStatus;
    /**
     * The detailed error message for the deployoment status result.
     */
    DeviceDeploymentStatusMessage?: String;
    /**
     * The description of the device.
     */
    Description?: DeviceDescription;
    /**
     * The time when the deployment on the device started.
     */
    DeploymentStartTime?: Timestamp;
  }
  export type DeviceDescription = string;
  export type DeviceFleetArn = string;
  export type DeviceFleetDescription = string;
  export type DeviceFleetSummaries = DeviceFleetSummary[];
  export interface DeviceFleetSummary {
    /**
     * Amazon Resource Name (ARN) of the device fleet.
     */
    DeviceFleetArn: DeviceFleetArn;
    /**
     * Name of the device fleet.
     */
    DeviceFleetName: EntityName;
    /**
     * Timestamp of when the device fleet was created.
     */
    CreationTime?: Timestamp;
    /**
     * Timestamp of when the device fleet was last updated.
     */
    LastModifiedTime?: Timestamp;
  }
  export type DeviceName = string;
  export type DeviceNames = DeviceName[];
  export interface DeviceSelectionConfig {
    /**
     * Type of device subsets to deploy to the current stage.
     */
    DeviceSubsetType: DeviceSubsetType;
    /**
     * Percentage of devices in the fleet to deploy to the current stage.
     */
    Percentage?: Percentage;
    /**
     * List of devices chosen to deploy.
     */
    DeviceNames?: DeviceNames;
    /**
     * A filter to select devices with names containing this name.
     */
    DeviceNameContains?: DeviceName;
  }
  export interface DeviceStats {
    /**
     * The number of devices connected with a heartbeat.
     */
    ConnectedDeviceCount: Long;
    /**
     * The number of registered devices.
     */
    RegisteredDeviceCount: Long;
  }
  export type DeviceSubsetType = "PERCENTAGE"|"SELECTION"|"NAMECONTAINS"|string;
  export type DeviceSummaries = DeviceSummary[];
  export interface DeviceSummary {
    /**
     * The unique identifier of the device.
     */
    DeviceName: EntityName;
    /**
     * Amazon Resource Name (ARN) of the device.
     */
    DeviceArn: DeviceArn;
    /**
     * A description of the device.
     */
    Description?: DeviceDescription;
    /**
     * The name of the fleet the device belongs to.
     */
    DeviceFleetName?: EntityName;
    /**
     * The Amazon Web Services Internet of Things (IoT) object thing name associated with the device..
     */
    IotThingName?: ThingName;
    /**
     * The timestamp of the last registration or de-reregistration.
     */
    RegistrationTime?: Timestamp;
    /**
     * The last heartbeat received from the device.
     */
    LatestHeartbeat?: Timestamp;
    /**
     * Models on the device.
     */
    Models?: EdgeModelSummaries;
    /**
     * Edge Manager agent version.
     */
    AgentVersion?: EdgeVersion;
  }
  export type Devices = Device[];
  export type Dimension = number;
  export interface DirectDeploySettings {
    /**
     * Describes whether model deployment permissions are enabled or disabled in the Canvas application.
     */
    Status?: FeatureStatus;
  }
  export type DirectInternetAccess = "Enabled"|"Disabled"|string;
  export type Direction = "Both"|"Ascendants"|"Descendants"|string;
  export type DirectoryPath = string;
  export type DisableProfiler = boolean;
  export interface DisableSagemakerServicecatalogPortfolioInput {
  }
  export interface DisableSagemakerServicecatalogPortfolioOutput {
  }
  export type DisassociateAdditionalCodeRepositories = boolean;
  export type DisassociateDefaultCodeRepository = boolean;
  export type DisassociateNotebookInstanceAcceleratorTypes = boolean;
  export type DisassociateNotebookInstanceLifecycleConfig = boolean;
  export interface DisassociateTrialComponentRequest {
    /**
     * The name of the component to disassociate from the trial.
     */
    TrialComponentName: ExperimentEntityName;
    /**
     * The name of the trial to disassociate from.
     */
    TrialName: ExperimentEntityName;
  }
  export interface DisassociateTrialComponentResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
  }
  export interface DockerSettings {
    /**
     * Indicates whether the domain can access Docker.
     */
    EnableDockerAccess?: FeatureStatus;
    /**
     * The list of Amazon Web Services accounts that are trusted when the domain is created in VPC-only mode.
     */
    VpcOnlyTrustedAccounts?: VpcOnlyTrustedAccounts;
  }
  export type DocumentSchemaVersion = string;
  export type Dollars = number;
  export type DomainArn = string;
  export interface DomainDetails {
    /**
     * The domain's Amazon Resource Name (ARN).
     */
    DomainArn?: DomainArn;
    /**
     * The domain ID.
     */
    DomainId?: DomainId;
    /**
     * The domain name.
     */
    DomainName?: DomainName;
    /**
     * The status.
     */
    Status?: DomainStatus;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The domain's URL.
     */
    Url?: String1024;
  }
  export type DomainId = string;
  export type DomainList = DomainDetails[];
  export type DomainName = string;
  export type DomainSecurityGroupIds = SecurityGroupId[];
  export interface DomainSettings {
    /**
     * The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
     */
    SecurityGroupIds?: DomainSecurityGroupIds;
    /**
     * A collection of settings that configure the RStudioServerPro Domain-level app.
     */
    RStudioServerProDomainSettings?: RStudioServerProDomainSettings;
    /**
     * The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key.
     */
    ExecutionRoleIdentityConfig?: ExecutionRoleIdentityConfig;
    /**
     * A collection of settings that configure the domain's Docker interaction.
     */
    DockerSettings?: DockerSettings;
    /**
     * A collection of settings that configure the Amazon Q experience within the domain. The AuthMode that you use to create the domain must be SSO.
     */
    AmazonQSettings?: AmazonQSettings;
  }
  export interface DomainSettingsForUpdate {
    /**
     * A collection of RStudioServerPro Domain-level app settings to update. A single RStudioServerPro application is created for a domain.
     */
    RStudioServerProDomainSettingsForUpdate?: RStudioServerProDomainSettingsForUpdate;
    /**
     * The configuration for attaching a SageMaker user profile name to the execution role as a sts:SourceIdentity key. This configuration can only be modified if there are no apps in the InService or Pending state.
     */
    ExecutionRoleIdentityConfig?: ExecutionRoleIdentityConfig;
    /**
     * The security groups for the Amazon Virtual Private Cloud that the Domain uses for communication between Domain-level apps and user apps.
     */
    SecurityGroupIds?: DomainSecurityGroupIds;
    /**
     * A collection of settings that configure the domain's Docker interaction.
     */
    DockerSettings?: DockerSettings;
    /**
     * A collection of settings that configure the Amazon Q experience within the domain.
     */
    AmazonQSettings?: AmazonQSettings;
  }
  export type DomainStatus = "Deleting"|"Failed"|"InService"|"Pending"|"Updating"|"Update_Failed"|"Delete_Failed"|string;
  export type Double = number;
  export type DoubleParameterValue = number;
  export interface DriftCheckBaselines {
    /**
     * Represents the drift check bias baselines that can be used when the model monitor is set using the model package. 
     */
    Bias?: DriftCheckBias;
    /**
     * Represents the drift check explainability baselines that can be used when the model monitor is set using the model package. 
     */
    Explainability?: DriftCheckExplainability;
    /**
     * Represents the drift check model quality baselines that can be used when the model monitor is set using the model package.
     */
    ModelQuality?: DriftCheckModelQuality;
    /**
     * Represents the drift check model data quality baselines that can be used when the model monitor is set using the model package.
     */
    ModelDataQuality?: DriftCheckModelDataQuality;
  }
  export interface DriftCheckBias {
    /**
     * The bias config file for a model.
     */
    ConfigFile?: FileSource;
    /**
     * The pre-training constraints.
     */
    PreTrainingConstraints?: MetricsSource;
    /**
     * The post-training constraints.
     */
    PostTrainingConstraints?: MetricsSource;
  }
  export interface DriftCheckExplainability {
    /**
     * The drift check explainability constraints.
     */
    Constraints?: MetricsSource;
    /**
     * The explainability config file for the model.
     */
    ConfigFile?: FileSource;
  }
  export interface DriftCheckModelDataQuality {
    /**
     * The drift check model data quality statistics.
     */
    Statistics?: MetricsSource;
    /**
     * The drift check model data quality constraints.
     */
    Constraints?: MetricsSource;
  }
  export interface DriftCheckModelQuality {
    /**
     * The drift check model quality statistics.
     */
    Statistics?: MetricsSource;
    /**
     * The drift check model quality constraints.
     */
    Constraints?: MetricsSource;
  }
  export interface DynamicScalingConfiguration {
    /**
     * The recommended minimum capacity to specify for your autoscaling policy.
     */
    MinCapacity?: Integer;
    /**
     * The recommended maximum capacity to specify for your autoscaling policy.
     */
    MaxCapacity?: Integer;
    /**
     * The recommended scale in cooldown time for your autoscaling policy.
     */
    ScaleInCooldown?: Integer;
    /**
     * The recommended scale out cooldown time for your autoscaling policy.
     */
    ScaleOutCooldown?: Integer;
    /**
     * An object of the scaling policies for each metric.
     */
    ScalingPolicies?: ScalingPolicies;
  }
  export interface EFSFileSystem {
    /**
     * The ID of your Amazon EFS file system.
     */
    FileSystemId: FileSystemId;
  }
  export interface EFSFileSystemConfig {
    /**
     * The ID of your Amazon EFS file system.
     */
    FileSystemId: FileSystemId;
    /**
     * The path to the file system directory that is accessible in Amazon SageMaker Studio. Permitted users can access only this directory and below.
     */
    FileSystemPath?: FileSystemPath;
  }
  export interface EMRStepMetadata {
    /**
     * The identifier of the EMR cluster.
     */
    ClusterId?: String256;
    /**
     * The identifier of the EMR cluster step.
     */
    StepId?: String256;
    /**
     * The name of the EMR cluster step.
     */
    StepName?: String256;
    /**
     * The path to the log file where the cluster step's failure root cause is recorded.
     */
    LogFilePath?: String1024;
  }
  export interface EbsStorageSettings {
    /**
     * The size of an EBS storage volume for a space.
     */
    EbsVolumeSizeInGb: SpaceEbsVolumeSizeInGb;
  }
  export interface Edge {
    /**
     * The Amazon Resource Name (ARN) of the source lineage entity of the directed edge.
     */
    SourceArn?: AssociationEntityArn;
    /**
     * The Amazon Resource Name (ARN) of the destination lineage entity of the directed edge.
     */
    DestinationArn?: AssociationEntityArn;
    /**
     * The type of the Association(Edge) between the source and destination. For example ContributedTo, Produced, or DerivedFrom.
     */
    AssociationType?: AssociationEdgeType;
  }
  export interface EdgeDeploymentConfig {
    /**
     * Toggle that determines whether to rollback to previous configuration if the current deployment fails. By default this is turned on. You may turn this off if you want to investigate the errors yourself.
     */
    FailureHandlingPolicy: FailureHandlingPolicy;
  }
  export interface EdgeDeploymentModelConfig {
    /**
     * The name the device application uses to reference this model.
     */
    ModelHandle: EntityName;
    /**
     * The edge packaging job associated with this deployment.
     */
    EdgePackagingJobName: EntityName;
  }
  export type EdgeDeploymentModelConfigs = EdgeDeploymentModelConfig[];
  export type EdgeDeploymentPlanArn = string;
  export type EdgeDeploymentPlanSummaries = EdgeDeploymentPlanSummary[];
  export interface EdgeDeploymentPlanSummary {
    /**
     * The ARN of the edge deployment plan.
     */
    EdgeDeploymentPlanArn: EdgeDeploymentPlanArn;
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * The name of the device fleet used for the deployment. 
     */
    DeviceFleetName: EntityName;
    /**
     * The number of edge devices with the successful deployment.
     */
    EdgeDeploymentSuccess: Integer;
    /**
     * The number of edge devices yet to pick up the deployment, or in progress.
     */
    EdgeDeploymentPending: Integer;
    /**
     * The number of edge devices that failed the deployment.
     */
    EdgeDeploymentFailed: Integer;
    /**
     * The time when the edge deployment plan was created.
     */
    CreationTime?: Timestamp;
    /**
     * The time when the edge deployment plan was last updated.
     */
    LastModifiedTime?: Timestamp;
  }
  export interface EdgeDeploymentStatus {
    /**
     * The general status of the current stage.
     */
    StageStatus: StageStatus;
    /**
     * The number of edge devices with the successful deployment in the current stage.
     */
    EdgeDeploymentSuccessInStage: Integer;
    /**
     * The number of edge devices yet to pick up the deployment in current stage, or in progress.
     */
    EdgeDeploymentPendingInStage: Integer;
    /**
     * The number of edge devices that failed the deployment in current stage.
     */
    EdgeDeploymentFailedInStage: Integer;
    /**
     * A detailed message about deployment status in current stage.
     */
    EdgeDeploymentStatusMessage?: String;
    /**
     * The time when the deployment API started.
     */
    EdgeDeploymentStageStartTime?: Timestamp;
  }
  export interface EdgeModel {
    /**
     * The name of the model.
     */
    ModelName: EntityName;
    /**
     * The model version.
     */
    ModelVersion: EdgeVersion;
    /**
     * The timestamp of the last data sample taken.
     */
    LatestSampleTime?: Timestamp;
    /**
     * The timestamp of the last inference that was made.
     */
    LatestInference?: Timestamp;
  }
  export interface EdgeModelStat {
    /**
     * The name of the model.
     */
    ModelName: EntityName;
    /**
     * The model version.
     */
    ModelVersion: EdgeVersion;
    /**
     * The number of devices that have this model version and do not have a heart beat.
     */
    OfflineDeviceCount: Long;
    /**
     * The number of devices that have this model version and have a heart beat. 
     */
    ConnectedDeviceCount: Long;
    /**
     * The number of devices that have this model version, a heart beat, and are currently running.
     */
    ActiveDeviceCount: Long;
    /**
     * The number of devices with this model version and are producing sample data.
     */
    SamplingDeviceCount: Long;
  }
  export type EdgeModelStats = EdgeModelStat[];
  export type EdgeModelSummaries = EdgeModelSummary[];
  export interface EdgeModelSummary {
    /**
     * The name of the model.
     */
    ModelName: EntityName;
    /**
     * The version model.
     */
    ModelVersion: EdgeVersion;
  }
  export type EdgeModels = EdgeModel[];
  export interface EdgeOutputConfig {
    /**
     * The Amazon Simple Storage (S3) bucker URI.
     */
    S3OutputLocation: S3Uri;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The deployment type SageMaker Edge Manager will create. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
     */
    PresetDeploymentType?: EdgePresetDeploymentType;
    /**
     * The configuration used to create deployment artifacts. Specify configuration options with a JSON string. The available configuration options for each type are:    ComponentName (optional) - Name of the GreenGrass V2 component. If not specified, the default name generated consists of "SagemakerEdgeManager" and the name of your SageMaker Edge Manager packaging job.    ComponentDescription (optional) - Description of the component.    ComponentVersion (optional) - The version of the component.  Amazon Web Services IoT Greengrass uses semantic versions for components. Semantic versions follow a major.minor.patch number system. For example, version 1.0.0 represents the first major release for a component. For more information, see the semantic version specification.     PlatformOS (optional) - The name of the operating system for the platform. Supported platforms include Windows and Linux.    PlatformArchitecture (optional) - The processor architecture for the platform.  Supported architectures Windows include: Windows32_x86, Windows64_x64. Supported architectures for Linux include: Linux x86_64, Linux ARMV8.  
     */
    PresetDeploymentConfig?: String;
  }
  export type EdgePackagingJobArn = string;
  export type EdgePackagingJobStatus = "STARTING"|"INPROGRESS"|"COMPLETED"|"FAILED"|"STOPPING"|"STOPPED"|string;
  export type EdgePackagingJobSummaries = EdgePackagingJobSummary[];
  export interface EdgePackagingJobSummary {
    /**
     * The Amazon Resource Name (ARN) of the edge packaging job.
     */
    EdgePackagingJobArn: EdgePackagingJobArn;
    /**
     * The name of the edge packaging job.
     */
    EdgePackagingJobName: EntityName;
    /**
     * The status of the edge packaging job.
     */
    EdgePackagingJobStatus: EdgePackagingJobStatus;
    /**
     * The name of the SageMaker Neo compilation job.
     */
    CompilationJobName?: EntityName;
    /**
     * The name of the model.
     */
    ModelName?: EntityName;
    /**
     * The version of the model.
     */
    ModelVersion?: EdgeVersion;
    /**
     * The timestamp of when the job was created.
     */
    CreationTime?: Timestamp;
    /**
     * The timestamp of when the edge packaging job was last updated.
     */
    LastModifiedTime?: Timestamp;
  }
  export type EdgePresetDeploymentArtifact = string;
  export interface EdgePresetDeploymentOutput {
    /**
     * The deployment type created by SageMaker Edge Manager. Currently only supports Amazon Web Services IoT Greengrass Version 2 components.
     */
    Type: EdgePresetDeploymentType;
    /**
     * The Amazon Resource Name (ARN) of the generated deployable resource.
     */
    Artifact?: EdgePresetDeploymentArtifact;
    /**
     * The status of the deployable resource.
     */
    Status?: EdgePresetDeploymentStatus;
    /**
     * Returns a message describing the status of the deployed resource.
     */
    StatusMessage?: String;
  }
  export type EdgePresetDeploymentStatus = "COMPLETED"|"FAILED"|string;
  export type EdgePresetDeploymentType = "GreengrassV2Component"|string;
  export type EdgeVersion = string;
  export type Edges = Edge[];
  export type EfsUid = string;
  export interface EmrServerlessComputeConfig {
    /**
     * The ARN of the IAM role granting the AutoML job V2 the necessary permissions access policies to list, connect to, or manage EMR Serverless jobs. For detailed information about the required permissions of this role, see "How to configure AutoML to initiate a remote job on EMR Serverless for large datasets" in Create a regression or classification job for tabular data using the AutoML API or Create an AutoML job for time-series forecasting using the API.
     */
    ExecutionRoleARN: RoleArn;
  }
  export interface EmrServerlessSettings {
    /**
     * The Amazon Resource Name (ARN) of the Amazon Web Services IAM role that is assumed for running Amazon EMR Serverless jobs in SageMaker Canvas. This role should have the necessary permissions to read and write data attached and a trust relationship with EMR Serverless.
     */
    ExecutionRoleArn?: RoleArn;
    /**
     * Describes whether Amazon EMR Serverless job capabilities are enabled or disabled in the SageMaker Canvas application.
     */
    Status?: FeatureStatus;
  }
  export interface EmrSettings {
    /**
     * An array of Amazon Resource Names (ARNs) of the IAM roles that the execution role of SageMaker can assume for performing operations or tasks related to Amazon EMR clusters or Amazon EMR Serverless applications. These roles define the permissions and access policies required when performing Amazon EMR-related operations, such as listing, connecting to, or terminating Amazon EMR clusters or Amazon EMR Serverless applications. They are typically used in cross-account access scenarios, where the Amazon EMR resources (clusters or serverless applications) are located in a different Amazon Web Services account than the SageMaker domain.
     */
    AssumableRoleArns?: AssumableRoleArns;
    /**
     * An array of Amazon Resource Names (ARNs) of the IAM roles used by the Amazon EMR cluster instances or job execution environments to access other Amazon Web Services services and resources needed during the runtime of your Amazon EMR or Amazon EMR Serverless workloads, such as Amazon S3 for data access, Amazon CloudWatch for logging, or other Amazon Web Services services based on the particular workload requirements.
     */
    ExecutionRoleArns?: ExecutionRoleArns;
  }
  export type EnableCapture = boolean;
  export type EnableInfraCheck = boolean;
  export type EnableIotRoleAlias = boolean;
  export type EnableRemoteDebug = boolean;
  export interface EnableSagemakerServicecatalogPortfolioInput {
  }
  export interface EnableSagemakerServicecatalogPortfolioOutput {
  }
  export type EnableSessionTagChaining = boolean;
  export type EnabledOrDisabled = "Enabled"|"Disabled"|string;
  export interface Endpoint {
    /**
     * The name of the endpoint.
     */
    EndpointName: EndpointName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
    /**
     * The endpoint configuration associated with the endpoint.
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * A list of the production variants hosted on the endpoint. Each production variant is a model.
     */
    ProductionVariants?: ProductionVariantSummaryList;
    DataCaptureConfig?: DataCaptureConfigSummary;
    /**
     * The status of the endpoint.
     */
    EndpointStatus: EndpointStatus;
    /**
     * If the endpoint failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The time that the endpoint was created.
     */
    CreationTime: Timestamp;
    /**
     * The last time the endpoint was modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * A list of monitoring schedules for the endpoint. For information about model monitoring, see Amazon SageMaker Model Monitor.
     */
    MonitoringSchedules?: MonitoringScheduleList;
    /**
     * A list of the tags associated with the endpoint. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
    /**
     * A list of the shadow variants hosted on the endpoint. Each shadow variant is a model in shadow mode with production traffic replicated from the production variant.
     */
    ShadowProductionVariants?: ProductionVariantSummaryList;
  }
  export type EndpointArn = string;
  export type EndpointConfigArn = string;
  export type EndpointConfigName = string;
  export type EndpointConfigNameContains = string;
  export type EndpointConfigSortKey = "Name"|"CreationTime"|string;
  export interface EndpointConfigStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the endpoint configuration used in the step.
     */
    Arn?: EndpointConfigArn;
  }
  export interface EndpointConfigSummary {
    /**
     * The name of the endpoint configuration.
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint configuration.
     */
    EndpointConfigArn: EndpointConfigArn;
    /**
     * A timestamp that shows when the endpoint configuration was created.
     */
    CreationTime: Timestamp;
  }
  export type EndpointConfigSummaryList = EndpointConfigSummary[];
  export interface EndpointInfo {
    /**
     * The name of a customer's endpoint.
     */
    EndpointName?: EndpointName;
  }
  export interface EndpointInput {
    /**
     * An endpoint in customer's account which has enabled DataCaptureConfig enabled.
     */
    EndpointName: EndpointName;
    /**
     * Path to the filesystem where the endpoint data is available to the container.
     */
    LocalPath: ProcessingLocalPath;
    /**
     * Whether the Pipe or File is used as the input mode for transferring data for the monitoring job. Pipe mode is recommended for large datasets. File mode is useful for small files that fit in memory. Defaults to File.
     */
    S3InputMode?: ProcessingS3InputMode;
    /**
     * Whether input data distributed in Amazon S3 is fully replicated or sharded by an Amazon S3 key. Defaults to FullyReplicated 
     */
    S3DataDistributionType?: ProcessingS3DataDistributionType;
    /**
     * The attributes of the input data that are the input features.
     */
    FeaturesAttribute?: String;
    /**
     * The attribute of the input data that represents the ground truth label.
     */
    InferenceAttribute?: String;
    /**
     * In a classification problem, the attribute that represents the class probability.
     */
    ProbabilityAttribute?: String;
    /**
     * The threshold for the class probability to be evaluated as a positive result.
     */
    ProbabilityThresholdAttribute?: ProbabilityThresholdAttribute;
    /**
     * If specified, monitoring jobs substract this time from the start time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
     */
    StartTimeOffset?: MonitoringTimeOffsetString;
    /**
     * If specified, monitoring jobs substract this time from the end time. For information about using offsets for scheduling monitoring jobs, see Schedule Model Quality Monitoring Jobs.
     */
    EndTimeOffset?: MonitoringTimeOffsetString;
    /**
     * The attributes of the input data to exclude from the analysis.
     */
    ExcludeFeaturesAttribute?: ExcludeFeaturesAttribute;
  }
  export interface EndpointInputConfiguration {
    /**
     * The instance types to use for the load test.
     */
    InstanceType?: ProductionVariantInstanceType;
    ServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * The inference specification name in the model package version.
     */
    InferenceSpecificationName?: InferenceSpecificationName;
    /**
     *  The parameter you want to benchmark against.
     */
    EnvironmentParameterRanges?: EnvironmentParameterRanges;
  }
  export type EndpointInputConfigurations = EndpointInputConfiguration[];
  export interface EndpointMetadata {
    /**
     * The name of the endpoint.
     */
    EndpointName: EndpointName;
    /**
     * The name of the endpoint configuration.
     */
    EndpointConfigName?: EndpointConfigName;
    /**
     *  The status of the endpoint. For possible values of the status of an endpoint, see EndpointSummary. 
     */
    EndpointStatus?: EndpointStatus;
    /**
     *  If the status of the endpoint is Failed, or the status is InService but update operation fails, this provides the reason why it failed. 
     */
    FailureReason?: FailureReason;
  }
  export type EndpointName = string;
  export type EndpointNameContains = string;
  export interface EndpointOutputConfiguration {
    /**
     * The name of the endpoint made during a recommendation job.
     */
    EndpointName: String;
    /**
     * The name of the production variant (deployed model) made during a recommendation job.
     */
    VariantName: String;
    /**
     * The instance type recommended by Amazon SageMaker Inference Recommender.
     */
    InstanceType?: ProductionVariantInstanceType;
    /**
     * The number of instances recommended to launch initially.
     */
    InitialInstanceCount?: InitialInstanceCount;
    ServerlessConfig?: ProductionVariantServerlessConfig;
  }
  export interface EndpointPerformance {
    /**
     * The metrics for an existing endpoint.
     */
    Metrics: InferenceMetrics;
    EndpointInfo: EndpointInfo;
  }
  export type EndpointPerformances = EndpointPerformance[];
  export type EndpointSortKey = "Name"|"CreationTime"|"Status"|string;
  export type EndpointStatus = "OutOfService"|"Creating"|"Updating"|"SystemUpdating"|"RollingBack"|"InService"|"Deleting"|"Failed"|"UpdateRollbackFailed"|string;
  export interface EndpointStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the endpoint in the step.
     */
    Arn?: EndpointArn;
  }
  export interface EndpointSummary {
    /**
     * The name of the endpoint.
     */
    EndpointName: EndpointName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
    /**
     * A timestamp that shows when the endpoint was created.
     */
    CreationTime: Timestamp;
    /**
     * A timestamp that shows when the endpoint was last modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The status of the endpoint.    OutOfService: Endpoint is not available to take incoming requests.    Creating: CreateEndpoint is executing.    Updating: UpdateEndpoint or UpdateEndpointWeightsAndCapacities is executing.    SystemUpdating: Endpoint is undergoing maintenance and cannot be updated or deleted or re-scaled until it has completed. This maintenance operation does not change any customer-specified values such as VPC config, KMS encryption, model, instance type, or instance count.    RollingBack: Endpoint fails to scale up or down or change its variant weight and is in the process of rolling back to its previous configuration. Once the rollback completes, endpoint returns to an InService status. This transitional status only applies to an endpoint that has autoscaling enabled and is undergoing variant weight or capacity changes as part of an UpdateEndpointWeightsAndCapacities call or when the UpdateEndpointWeightsAndCapacities operation is called explicitly.    InService: Endpoint is available to process incoming requests.    Deleting: DeleteEndpoint is executing.    Failed: Endpoint could not be created, updated, or re-scaled. Use DescribeEndpointOutput$FailureReason for information about the failure. DeleteEndpoint is the only operation that can be performed on a failed endpoint.   To get a list of endpoints with a specified status, use the StatusEquals filter with a call to ListEndpoints.
     */
    EndpointStatus: EndpointStatus;
  }
  export type EndpointSummaryList = EndpointSummary[];
  export type Endpoints = EndpointInfo[];
  export type EntityDescription = string;
  export type EntityName = string;
  export type EnvironmentKey = string;
  export type EnvironmentMap = {[key: string]: EnvironmentValue};
  export interface EnvironmentParameter {
    /**
     * The environment key suggested by the Amazon SageMaker Inference Recommender.
     */
    Key: String;
    /**
     * The value type suggested by the Amazon SageMaker Inference Recommender.
     */
    ValueType: String;
    /**
     * The value suggested by the Amazon SageMaker Inference Recommender.
     */
    Value: String;
  }
  export interface EnvironmentParameterRanges {
    /**
     * Specified a list of parameters for each category.
     */
    CategoricalParameterRanges?: CategoricalParameters;
  }
  export type EnvironmentParameters = EnvironmentParameter[];
  export type EnvironmentValue = string;
  export type ExcludeFeaturesAttribute = string;
  export type ExecutionRoleArns = RoleArn[];
  export type ExecutionRoleIdentityConfig = "USER_PROFILE_NAME"|"DISABLED"|string;
  export type ExecutionStatus = "Pending"|"Completed"|"CompletedWithViolations"|"InProgress"|"Failed"|"Stopping"|"Stopped"|string;
  export type ExitMessage = string;
  export interface Experiment {
    /**
     * The name of the experiment.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the experiment.
     */
    ExperimentArn?: ExperimentArn;
    /**
     * The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    Source?: ExperimentSource;
    /**
     * The description of the experiment.
     */
    Description?: ExperimentDescription;
    /**
     * When the experiment was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the experiment.
     */
    CreatedBy?: UserContext;
    /**
     * When the experiment was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * The list of tags that are associated with the experiment. You can use Search API to search on the tags.
     */
    Tags?: TagList;
  }
  export type ExperimentArn = string;
  export interface ExperimentConfig {
    /**
     * The name of an existing experiment to associate with the trial component.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The name of an existing trial to associate the trial component with. If not specified, a new trial is created.
     */
    TrialName?: ExperimentEntityName;
    /**
     * The display name for the trial component. If this key isn't specified, the display name is the trial component name.
     */
    TrialComponentDisplayName?: ExperimentEntityName;
    /**
     * The name of the experiment run to associate with the trial component.
     */
    RunName?: ExperimentEntityName;
  }
  export type ExperimentDescription = string;
  export type ExperimentEntityName = string;
  export type ExperimentEntityNameOrArn = string;
  export interface ExperimentSource {
    /**
     * The Amazon Resource Name (ARN) of the source.
     */
    SourceArn: ExperimentSourceArn;
    /**
     * The source type.
     */
    SourceType?: SourceType;
  }
  export type ExperimentSourceArn = string;
  export type ExperimentSummaries = ExperimentSummary[];
  export interface ExperimentSummary {
    /**
     * The Amazon Resource Name (ARN) of the experiment.
     */
    ExperimentArn?: ExperimentArn;
    /**
     * The name of the experiment.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The name of the experiment as displayed. If DisplayName isn't specified, ExperimentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    ExperimentSource?: ExperimentSource;
    /**
     * When the experiment was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the experiment was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type ExpiresInSeconds = number;
  export interface Explainability {
    /**
     * The explainability report for a model.
     */
    Report?: MetricsSource;
  }
  export type ExplainabilityLocation = string;
  export interface ExplainerConfig {
    /**
     * A member of ExplainerConfig that contains configuration parameters for the SageMaker Clarify explainer.
     */
    ClarifyExplainerConfig?: ClarifyExplainerConfig;
  }
  export interface FailStepMetadata {
    /**
     * A message that you define and then is processed and rendered by the Fail step when the error occurs.
     */
    ErrorMessage?: String3072;
  }
  export type FailureHandlingPolicy = "ROLLBACK_ON_FAILURE"|"DO_NOTHING"|string;
  export type FailureReason = string;
  export type FeatureAdditions = FeatureDefinition[];
  export interface FeatureDefinition {
    /**
     * The name of a feature. The type must be a string. FeatureName cannot be any of the following: is_deleted, write_time, api_invocation_time. The name:   Must start with an alphanumeric character.   Can only include alphanumeric characters, underscores, and hyphens. Spaces are not allowed.  
     */
    FeatureName: FeatureName;
    /**
     * The value type of a feature. Valid values are Integral, Fractional, or String.
     */
    FeatureType: FeatureType;
    /**
     * A grouping of elements where each element within the collection must have the same feature type (String, Integral, or Fractional).    List: An ordered collection of elements.    Set: An unordered collection of unique elements.    Vector: A specialized list that represents a fixed-size array of elements. The vector dimension is determined by you. Must have elements with fractional feature types.   
     */
    CollectionType?: CollectionType;
    /**
     * Configuration for your collection.
     */
    CollectionConfig?: CollectionConfig;
  }
  export type FeatureDefinitions = FeatureDefinition[];
  export type FeatureDescription = string;
  export interface FeatureGroup {
    /**
     * The Amazon Resource Name (ARN) of a FeatureGroup.
     */
    FeatureGroupArn?: FeatureGroupArn;
    /**
     * The name of the FeatureGroup.
     */
    FeatureGroupName?: FeatureGroupName;
    /**
     * The name of the Feature whose value uniquely identifies a Record defined in the FeatureGroup FeatureDefinitions.
     */
    RecordIdentifierFeatureName?: FeatureName;
    /**
     * The name of the feature that stores the EventTime of a Record in a FeatureGroup. A EventTime is point in time when a new event occurs that corresponds to the creation or update of a Record in FeatureGroup. All Records in the FeatureGroup must have a corresponding EventTime.
     */
    EventTimeFeatureName?: FeatureName;
    /**
     * A list of Features. Each Feature must include a FeatureName and a FeatureType.  Valid FeatureTypes are Integral, Fractional and String.   FeatureNames cannot be any of the following: is_deleted, write_time, api_invocation_time. You can create up to 2,500 FeatureDefinitions per FeatureGroup.
     */
    FeatureDefinitions?: FeatureDefinitions;
    /**
     * The time a FeatureGroup was created.
     */
    CreationTime?: CreationTime;
    /**
     * A timestamp indicating the last time you updated the feature group.
     */
    LastModifiedTime?: LastModifiedTime;
    OnlineStoreConfig?: OnlineStoreConfig;
    OfflineStoreConfig?: OfflineStoreConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM execution role used to create the feature group.
     */
    RoleArn?: RoleArn;
    /**
     * A FeatureGroup status.
     */
    FeatureGroupStatus?: FeatureGroupStatus;
    OfflineStoreStatus?: OfflineStoreStatus;
    /**
     * A value that indicates whether the feature group was updated successfully.
     */
    LastUpdateStatus?: LastUpdateStatus;
    /**
     * The reason that the FeatureGroup failed to be replicated in the OfflineStore. This is failure may be due to a failure to create a FeatureGroup in or delete a FeatureGroup from the OfflineStore.
     */
    FailureReason?: FailureReason;
    /**
     * A free form description of a FeatureGroup.
     */
    Description?: Description;
    /**
     * Tags used to define a FeatureGroup.
     */
    Tags?: TagList;
  }
  export type FeatureGroupArn = string;
  export type FeatureGroupMaxResults = number;
  export type FeatureGroupName = string;
  export type FeatureGroupNameContains = string;
  export type FeatureGroupNameOrArn = string;
  export type FeatureGroupSortBy = "Name"|"FeatureGroupStatus"|"OfflineStoreStatus"|"CreationTime"|string;
  export type FeatureGroupSortOrder = "Ascending"|"Descending"|string;
  export type FeatureGroupStatus = "Creating"|"Created"|"CreateFailed"|"Deleting"|"DeleteFailed"|string;
  export type FeatureGroupSummaries = FeatureGroupSummary[];
  export interface FeatureGroupSummary {
    /**
     * The name of FeatureGroup.
     */
    FeatureGroupName: FeatureGroupName;
    /**
     * Unique identifier for the FeatureGroup.
     */
    FeatureGroupArn: FeatureGroupArn;
    /**
     * A timestamp indicating the time of creation time of the FeatureGroup.
     */
    CreationTime: Timestamp;
    /**
     * The status of a FeatureGroup. The status can be any of the following: Creating, Created, CreateFail, Deleting or DetailFail. 
     */
    FeatureGroupStatus?: FeatureGroupStatus;
    /**
     * Notifies you if replicating data into the OfflineStore has failed. Returns either: Active or Blocked.
     */
    OfflineStoreStatus?: OfflineStoreStatus;
  }
  export interface FeatureMetadata {
    /**
     * The Amazon Resource Number (ARN) of the feature group.
     */
    FeatureGroupArn?: FeatureGroupArn;
    /**
     * The name of the feature group containing the feature.
     */
    FeatureGroupName?: FeatureGroupName;
    /**
     * The name of feature.
     */
    FeatureName?: FeatureName;
    /**
     * The data type of the feature.
     */
    FeatureType?: FeatureType;
    /**
     * A timestamp indicating when the feature was created.
     */
    CreationTime?: CreationTime;
    /**
     * A timestamp indicating when the feature was last modified.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * An optional description that you specify to better describe the feature.
     */
    Description?: FeatureDescription;
    /**
     * Optional key-value pairs that you specify to better describe the feature.
     */
    Parameters?: FeatureParameters;
  }
  export type FeatureName = string;
  export interface FeatureParameter {
    /**
     * A key that must contain a value to describe the feature.
     */
    Key?: FeatureParameterKey;
    /**
     * The value that belongs to a key.
     */
    Value?: FeatureParameterValue;
  }
  export type FeatureParameterAdditions = FeatureParameter[];
  export type FeatureParameterKey = string;
  export type FeatureParameterRemovals = FeatureParameterKey[];
  export type FeatureParameterValue = string;
  export type FeatureParameters = FeatureParameter[];
  export type FeatureStatus = "ENABLED"|"DISABLED"|string;
  export type FeatureType = "Integral"|"Fractional"|"String"|string;
  export interface FileSource {
    /**
     * The type of content stored in the file source.
     */
    ContentType?: ContentType;
    /**
     * The digest of the file source.
     */
    ContentDigest?: ContentDigest;
    /**
     * The Amazon S3 URI for the file source.
     */
    S3Uri: S3Uri;
  }
  export type FileSystemAccessMode = "rw"|"ro"|string;
  export interface FileSystemConfig {
    /**
     * The path within the image to mount the user's EFS home directory. The directory should be empty. If not specified, defaults to /home/sagemaker-user.
     */
    MountPath?: MountPath;
    /**
     * The default POSIX user ID (UID). If not specified, defaults to 1000.
     */
    DefaultUid?: DefaultUid;
    /**
     * The default POSIX group ID (GID). If not specified, defaults to 100.
     */
    DefaultGid?: DefaultGid;
  }
  export interface FileSystemDataSource {
    /**
     * The file system id.
     */
    FileSystemId: FileSystemId;
    /**
     * The access mode of the mount of the directory associated with the channel. A directory can be mounted either in ro (read-only) or rw (read-write) mode.
     */
    FileSystemAccessMode: FileSystemAccessMode;
    /**
     * The file system type. 
     */
    FileSystemType: FileSystemType;
    /**
     * The full path to the directory to associate with the channel.
     */
    DirectoryPath: DirectoryPath;
  }
  export type FileSystemId = string;
  export type FileSystemPath = string;
  export type FileSystemType = "EFS"|"FSxLustre"|string;
  export type FillingTransformationMap = {[key: string]: FillingTransformationValue};
  export type FillingTransformationValue = string;
  export type FillingTransformations = {[key: string]: FillingTransformationMap};
  export type FillingType = "frontfill"|"middlefill"|"backfill"|"futurefill"|"frontfill_value"|"middlefill_value"|"backfill_value"|"futurefill_value"|string;
  export interface Filter {
    /**
     * A resource property name. For example, TrainingJobName. For valid property names, see SearchRecord. You must specify a valid property for the resource.
     */
    Name: ResourcePropertyName;
    /**
     * A Boolean binary operator that is used to evaluate the filter. The operator field contains one of the following values:  Equals  The value of Name equals Value.  NotEquals  The value of Name doesn't equal Value.  Exists  The Name property exists.  NotExists  The Name property does not exist.  GreaterThan  The value of Name is greater than Value. Not supported for text properties.  GreaterThanOrEqualTo  The value of Name is greater than or equal to Value. Not supported for text properties.  LessThan  The value of Name is less than Value. Not supported for text properties.  LessThanOrEqualTo  The value of Name is less than or equal to Value. Not supported for text properties.  In  The value of Name is one of the comma delimited strings in Value. Only supported for text properties.  Contains  The value of Name contains the string Value. Only supported for text properties. A SearchExpression can include the Contains operator multiple times when the value of Name is one of the following:    Experiment.DisplayName     Experiment.ExperimentName     Experiment.Tags     Trial.DisplayName     Trial.TrialName     Trial.Tags     TrialComponent.DisplayName     TrialComponent.TrialComponentName     TrialComponent.Tags     TrialComponent.InputArtifacts     TrialComponent.OutputArtifacts    A SearchExpression can include only one Contains operator for all other values of Name. In these cases, if you include multiple Contains operators in the SearchExpression, the result is the following error message: "'CONTAINS' operator usage limit of 1 exceeded."  
     */
    Operator?: Operator;
    /**
     * A value used with Name and Operator to determine which resources satisfy the filter's condition. For numerical properties, Value must be an integer or floating-point decimal. For timestamp properties, Value must be an ISO 8601 date-time string of the following format: YYYY-mm-dd'T'HH:MM:SS.
     */
    Value?: FilterValue;
  }
  export type FilterList = Filter[];
  export type FilterValue = string;
  export interface FinalAutoMLJobObjectiveMetric {
    /**
     * The type of metric with the best result.
     */
    Type?: AutoMLJobObjectiveType;
    /**
     * The name of the metric with the best result. For a description of the possible objective metrics, see AutoMLJobObjective$MetricName.
     */
    MetricName: AutoMLMetricEnum;
    /**
     * The value of the metric with the best result.
     */
    Value: MetricValue;
    /**
     * The name of the standard metric. For a description of the standard metrics, see Autopilot candidate metrics.
     */
    StandardMetricName?: AutoMLMetricEnum;
  }
  export interface FinalHyperParameterTuningJobObjectiveMetric {
    /**
     * Select if you want to minimize or maximize the objective metric during hyperparameter tuning. 
     */
    Type?: HyperParameterTuningJobObjectiveType;
    /**
     * The name of the objective metric. For SageMaker built-in algorithms, metrics are defined per algorithm. See the metrics for XGBoost as an example. You can also use a custom algorithm for training and define your own metrics. For more information, see Define metrics and environment variables.
     */
    MetricName: MetricName;
    /**
     * The value of the objective metric.
     */
    Value: MetricValue;
  }
  export type FinalMetricDataList = MetricData[];
  export type FlatInvocations = "Continue"|"Stop"|string;
  export type Float = number;
  export type FlowDefinitionArn = string;
  export type FlowDefinitionName = string;
  export interface FlowDefinitionOutputConfig {
    /**
     * The Amazon S3 path where the object containing human output will be made available. To learn more about the format of Amazon A2I output data, see Amazon A2I Output Data.
     */
    S3OutputPath: S3Uri;
    /**
     * The Amazon Key Management Service (KMS) key ID for server-side encryption.
     */
    KmsKeyId?: KmsKeyId;
  }
  export type FlowDefinitionStatus = "Initializing"|"Active"|"Failed"|"Deleting"|string;
  export type FlowDefinitionSummaries = FlowDefinitionSummary[];
  export interface FlowDefinitionSummary {
    /**
     * The name of the flow definition.
     */
    FlowDefinitionName: FlowDefinitionName;
    /**
     * The Amazon Resource Name (ARN) of the flow definition.
     */
    FlowDefinitionArn: FlowDefinitionArn;
    /**
     * The status of the flow definition. Valid values:
     */
    FlowDefinitionStatus: FlowDefinitionStatus;
    /**
     * The timestamp when SageMaker created the flow definition.
     */
    CreationTime: Timestamp;
    /**
     * The reason why the flow definition creation failed. A failure reason is returned only when the flow definition status is Failed.
     */
    FailureReason?: FailureReason;
  }
  export type FlowDefinitionTaskAvailabilityLifetimeInSeconds = number;
  export type FlowDefinitionTaskCount = number;
  export type FlowDefinitionTaskDescription = string;
  export type FlowDefinitionTaskKeyword = string;
  export type FlowDefinitionTaskKeywords = FlowDefinitionTaskKeyword[];
  export type FlowDefinitionTaskTimeLimitInSeconds = number;
  export type FlowDefinitionTaskTitle = string;
  export type ForecastFrequency = string;
  export type ForecastHorizon = number;
  export type ForecastQuantile = string;
  export type ForecastQuantiles = ForecastQuantile[];
  export type Framework = "TENSORFLOW"|"KERAS"|"MXNET"|"ONNX"|"PYTORCH"|"XGBOOST"|"TFLITE"|"DARKNET"|"SKLEARN"|string;
  export type FrameworkVersion = string;
  export type GenerateCandidateDefinitionsOnly = boolean;
  export interface GenerativeAiSettings {
    /**
     * The ARN of an Amazon Web Services IAM role that allows fine-tuning of large language models (LLMs) in Amazon Bedrock. The IAM role should have Amazon S3 read and write permissions, as well as a trust relationship that establishes bedrock.amazonaws.com as a service principal.
     */
    AmazonBedrockRoleArn?: RoleArn;
  }
  export interface GetDeviceFleetReportRequest {
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
  }
  export interface GetDeviceFleetReportResponse {
    /**
     * The Amazon Resource Name (ARN) of the device.
     */
    DeviceFleetArn: DeviceFleetArn;
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
    /**
     * The output configuration for storing sample data collected by the fleet.
     */
    OutputConfig?: EdgeOutputConfig;
    /**
     * Description of the fleet.
     */
    Description?: DeviceFleetDescription;
    /**
     * Timestamp of when the report was generated.
     */
    ReportGenerated?: Timestamp;
    /**
     * Status of devices.
     */
    DeviceStats?: DeviceStats;
    /**
     * The versions of Edge Manager agent deployed on the fleet.
     */
    AgentVersions?: AgentVersions;
    /**
     * Status of model on device.
     */
    ModelStats?: EdgeModelStats;
  }
  export interface GetLineageGroupPolicyRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupName: LineageGroupNameOrArn;
  }
  export interface GetLineageGroupPolicyResponse {
    /**
     * The Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupArn?: LineageGroupArn;
    /**
     * The resource policy that gives access to the lineage group in another account.
     */
    ResourcePolicy?: ResourcePolicyString;
  }
  export interface GetModelPackageGroupPolicyInput {
    /**
     * The name of the model group for which to get the resource policy.
     */
    ModelPackageGroupName: EntityName;
  }
  export interface GetModelPackageGroupPolicyOutput {
    /**
     * The resource policy for the model group.
     */
    ResourcePolicy: PolicyString;
  }
  export interface GetSagemakerServicecatalogPortfolioStatusInput {
  }
  export interface GetSagemakerServicecatalogPortfolioStatusOutput {
    /**
     * Whether Service Catalog is enabled or disabled in SageMaker.
     */
    Status?: SagemakerServicecatalogStatus;
  }
  export interface GetScalingConfigurationRecommendationRequest {
    /**
     * The name of a previously completed Inference Recommender job.
     */
    InferenceRecommendationsJobName: RecommendationJobName;
    /**
     * The recommendation ID of a previously completed inference recommendation. This ID should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field. Specify either this field or the EndpointName field.
     */
    RecommendationId?: String;
    /**
     * The name of an endpoint benchmarked during a previously completed inference recommendation job. This name should come from one of the recommendations returned by the job specified in the InferenceRecommendationsJobName field. Specify either this field or the RecommendationId field.
     */
    EndpointName?: EndpointName;
    /**
     * The percentage of how much utilization you want an instance to use before autoscaling. The default value is 50%.
     */
    TargetCpuUtilizationPerCore?: UtilizationPercentagePerCore;
    /**
     * An object where you specify the anticipated traffic pattern for an endpoint.
     */
    ScalingPolicyObjective?: ScalingPolicyObjective;
  }
  export interface GetScalingConfigurationRecommendationResponse {
    /**
     * The name of a previously completed Inference Recommender job.
     */
    InferenceRecommendationsJobName?: RecommendationJobName;
    /**
     * The recommendation ID of a previously completed inference recommendation.
     */
    RecommendationId?: String;
    /**
     * The name of an endpoint benchmarked during a previously completed Inference Recommender job.
     */
    EndpointName?: EndpointName;
    /**
     * The percentage of how much utilization you want an instance to use before autoscaling, which you specified in the request. The default value is 50%.
     */
    TargetCpuUtilizationPerCore?: UtilizationPercentagePerCore;
    /**
     * An object representing the anticipated traffic pattern for an endpoint that you specified in the request.
     */
    ScalingPolicyObjective?: ScalingPolicyObjective;
    /**
     * An object with a list of metrics that were benchmarked during the previously completed Inference Recommender job.
     */
    Metric?: ScalingPolicyMetric;
    /**
     * An object with the recommended values for you to specify when creating an autoscaling policy.
     */
    DynamicScalingConfiguration?: DynamicScalingConfiguration;
  }
  export interface GetSearchSuggestionsRequest {
    /**
     * The name of the SageMaker resource to search for.
     */
    Resource: ResourceType;
    /**
     * Limits the property names that are included in the response.
     */
    SuggestionQuery?: SuggestionQuery;
  }
  export interface GetSearchSuggestionsResponse {
    /**
     * A list of property names for a Resource that match a SuggestionQuery.
     */
    PropertyNameSuggestions?: PropertyNameSuggestionList;
  }
  export type Gid = number;
  export interface GitConfig {
    /**
     * The URL where the Git repository is located.
     */
    RepositoryUrl: GitConfigUrl;
    /**
     * The default branch for the Git repository.
     */
    Branch?: Branch;
    /**
     * The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:  {"username": UserName, "password": Password} 
     */
    SecretArn?: SecretArn;
  }
  export interface GitConfigForUpdate {
    /**
     * The Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the git repository. The secret must have a staging label of AWSCURRENT and must be in the following format:  {"username": UserName, "password": Password} 
     */
    SecretArn?: SecretArn;
  }
  export type GitConfigUrl = string;
  export type Group = string;
  export type GroupingAttributeName = string;
  export type GroupingAttributeNames = GroupingAttributeName[];
  export type Groups = Group[];
  export type HiddenAppTypesList = AppType[];
  export type HiddenMlToolsList = MlTools[];
  export type HolidayConfig = HolidayConfigAttributes[];
  export interface HolidayConfigAttributes {
    /**
     * The country code for the holiday calendar. For the list of public holiday calendars supported by AutoML job V2, see Country Codes. Use the country code corresponding to the country of your choice.
     */
    CountryCode?: CountryCode;
  }
  export type HookParameters = {[key: string]: ConfigValue};
  export type Horovod = boolean;
  export type HubArn = string;
  export type HubContentArn = string;
  export interface HubContentDependency {
    /**
     * The hub content dependency origin path.
     */
    DependencyOriginPath?: DependencyOriginPath;
    /**
     * The hub content dependency copy path.
     */
    DependencyCopyPath?: DependencyCopyPath;
  }
  export type HubContentDependencyList = HubContentDependency[];
  export type HubContentDescription = string;
  export type HubContentDisplayName = string;
  export type HubContentDocument = string;
  export interface HubContentInfo {
    /**
     * The name of the hub content.
     */
    HubContentName: HubContentName;
    /**
     * The Amazon Resource Name (ARN) of the hub content.
     */
    HubContentArn: HubContentArn;
    /**
     * The ARN of the public hub content.
     */
    SageMakerPublicHubContentArn?: SageMakerPublicHubContentArn;
    /**
     * The version of the hub content.
     */
    HubContentVersion: HubContentVersion;
    /**
     * The type of hub content.
     */
    HubContentType: HubContentType;
    /**
     * The version of the hub content document schema.
     */
    DocumentSchemaVersion: DocumentSchemaVersion;
    /**
     * The display name of the hub content.
     */
    HubContentDisplayName?: HubContentDisplayName;
    /**
     * A description of the hub content.
     */
    HubContentDescription?: HubContentDescription;
    /**
     * The support status of the hub content.
     */
    SupportStatus?: HubContentSupportStatus;
    /**
     * The searchable keywords for the hub content.
     */
    HubContentSearchKeywords?: HubContentSearchKeywordList;
    /**
     * The status of the hub content.
     */
    HubContentStatus: HubContentStatus;
    /**
     * The date and time that the hub content was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time when the hub content was originally created, before any updates or revisions.
     */
    OriginalCreationTime?: Timestamp;
  }
  export type HubContentInfoList = HubContentInfo[];
  export type HubContentMarkdown = string;
  export type HubContentName = string;
  export type HubContentSearchKeywordList = HubSearchKeyword[];
  export type HubContentSortBy = "HubContentName"|"CreationTime"|"HubContentStatus"|string;
  export type HubContentStatus = "Available"|"Importing"|"Deleting"|"ImportFailed"|"DeleteFailed"|string;
  export type HubContentSupportStatus = "Supported"|"Deprecated"|string;
  export type HubContentType = "Model"|"Notebook"|"ModelReference"|string;
  export type HubContentVersion = string;
  export type HubDescription = string;
  export type HubDisplayName = string;
  export interface HubInfo {
    /**
     * The name of the hub.
     */
    HubName: HubName;
    /**
     * The Amazon Resource Name (ARN) of the hub.
     */
    HubArn: HubArn;
    /**
     * The display name of the hub.
     */
    HubDisplayName?: HubDisplayName;
    /**
     * A description of the hub.
     */
    HubDescription?: HubDescription;
    /**
     * The searchable keywords for the hub.
     */
    HubSearchKeywords?: HubSearchKeywordList;
    /**
     * The status of the hub.
     */
    HubStatus: HubStatus;
    /**
     * The date and time that the hub was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the hub was last modified.
     */
    LastModifiedTime: Timestamp;
  }
  export type HubInfoList = HubInfo[];
  export type HubName = string;
  export type HubNameOrArn = string;
  export interface HubS3StorageConfig {
    /**
     * The Amazon S3 bucket prefix for hosting hub content.
     */
    S3OutputPath?: S3OutputPath;
  }
  export type HubSearchKeyword = string;
  export type HubSearchKeywordList = HubSearchKeyword[];
  export type HubSortBy = "HubName"|"CreationTime"|"HubStatus"|"AccountIdOwner"|string;
  export type HubStatus = "InService"|"Creating"|"Updating"|"Deleting"|"CreateFailed"|"UpdateFailed"|"DeleteFailed"|string;
  export type HumanLoopActivationConditions = string;
  export interface HumanLoopActivationConditionsConfig {
    /**
     * JSON expressing use-case specific conditions declaratively. If any condition is matched, atomic tasks are created against the configured work team. The set of conditions is different for Rekognition and Textract. For more information about how to structure the JSON, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI in the Amazon SageMaker Developer Guide.
     */
    HumanLoopActivationConditions: HumanLoopActivationConditions;
  }
  export interface HumanLoopActivationConfig {
    /**
     * Container structure for defining under what conditions SageMaker creates a human loop.
     */
    HumanLoopActivationConditionsConfig: HumanLoopActivationConditionsConfig;
  }
  export interface HumanLoopConfig {
    /**
     * Amazon Resource Name (ARN) of a team of workers. To learn more about the types of workforces and work teams you can create and use with Amazon A2I, see Create and Manage Workforces.
     */
    WorkteamArn: WorkteamArn;
    /**
     * The Amazon Resource Name (ARN) of the human task user interface. You can use standard HTML and Crowd HTML Elements to create a custom worker task template. You use this template to create a human task UI. To learn how to create a custom HTML template, see Create Custom Worker Task Template. To learn how to create a human task UI, which is a worker task template that can be used in a flow definition, see Create and Delete a Worker Task Templates.
     */
    HumanTaskUiArn: HumanTaskUiArn;
    /**
     * A title for the human worker task.
     */
    TaskTitle: FlowDefinitionTaskTitle;
    /**
     * A description for the human worker task.
     */
    TaskDescription: FlowDefinitionTaskDescription;
    /**
     * The number of distinct workers who will perform the same task on each object. For example, if TaskCount is set to 3 for an image classification labeling job, three workers will classify each input image. Increasing TaskCount can improve label accuracy.
     */
    TaskCount: FlowDefinitionTaskCount;
    /**
     * The length of time that a task remains available for review by human workers.
     */
    TaskAvailabilityLifetimeInSeconds?: FlowDefinitionTaskAvailabilityLifetimeInSeconds;
    /**
     * The amount of time that a worker has to complete a task. The default value is 3,600 seconds (1 hour).
     */
    TaskTimeLimitInSeconds?: FlowDefinitionTaskTimeLimitInSeconds;
    /**
     * Keywords used to describe the task so that workers can discover the task.
     */
    TaskKeywords?: FlowDefinitionTaskKeywords;
    PublicWorkforceTaskPrice?: PublicWorkforceTaskPrice;
  }
  export interface HumanLoopRequestSource {
    /**
     * Specifies whether Amazon Rekognition or Amazon Textract are used as the integration source. The default field settings and JSON parsing rules are different based on the integration source. Valid values:
     */
    AwsManagedHumanLoopRequestSource: AwsManagedHumanLoopRequestSource;
  }
  export interface HumanTaskConfig {
    /**
     * The Amazon Resource Name (ARN) of the work team assigned to complete the tasks.
     */
    WorkteamArn: WorkteamArn;
    /**
     * Information about the user interface that workers use to complete the labeling task.
     */
    UiConfig: UiConfig;
    /**
     * The Amazon Resource Name (ARN) of a Lambda function that is run before a data object is sent to a human worker. Use this function to provide input to a custom labeling job. For built-in task types, use one of the following Amazon SageMaker Ground Truth Lambda function ARNs for PreHumanTaskLambdaArn. For custom labeling workflows, see Pre-annotation Lambda.   Bounding box - Finds the most similar boxes from different workers based on the Jaccard index of the boxes.    arn:aws:lambda:us-east-1:432418664414:function:PRE-BoundingBox     arn:aws:lambda:us-east-2:266458841044:function:PRE-BoundingBox     arn:aws:lambda:us-west-2:081040173940:function:PRE-BoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:PRE-BoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:PRE-BoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:PRE-BoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:PRE-BoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-BoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-BoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:PRE-BoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-BoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-BoundingBox     Image classification - Uses a variant of the Expectation Maximization approach to estimate the true class of an image based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClass     arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClass     arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClass     Multi-label image classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of an image based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:us-east-2:266458841044:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:us-west-2:081040173940:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ca-central-1:918755190332:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:eu-west-1:568282634449:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:eu-west-2:487402164563:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:eu-central-1:203001061592:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ap-south-1:565803892007:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-ImageMultiClassMultiLabel     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-ImageMultiClassMultiLabel     Semantic segmentation - Treats each pixel in an image as a multi-class classification and treats pixel annotations from workers as "votes" for the correct label.    arn:aws:lambda:us-east-1:432418664414:function:PRE-SemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:PRE-SemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:PRE-SemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:PRE-SemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:PRE-SemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:PRE-SemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:PRE-SemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-SemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-SemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:PRE-SemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-SemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-SemanticSegmentation     Text classification - Uses a variant of the Expectation Maximization approach to estimate the true class of text based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClass     arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClass     arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClass     Multi-label text classification - Uses a variant of the Expectation Maximization approach to estimate the true classes of text based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:us-east-2:266458841044:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:us-west-2:081040173940:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ca-central-1:918755190332:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:eu-west-1:568282634449:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:eu-west-2:487402164563:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:eu-central-1:203001061592:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ap-south-1:565803892007:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-TextMultiClassMultiLabel     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-TextMultiClassMultiLabel     Named entity recognition - Groups similar selections and calculates aggregate boundaries, resolving to most-assigned label.    arn:aws:lambda:us-east-1:432418664414:function:PRE-NamedEntityRecognition     arn:aws:lambda:us-east-2:266458841044:function:PRE-NamedEntityRecognition     arn:aws:lambda:us-west-2:081040173940:function:PRE-NamedEntityRecognition     arn:aws:lambda:ca-central-1:918755190332:function:PRE-NamedEntityRecognition     arn:aws:lambda:eu-west-1:568282634449:function:PRE-NamedEntityRecognition     arn:aws:lambda:eu-west-2:487402164563:function:PRE-NamedEntityRecognition     arn:aws:lambda:eu-central-1:203001061592:function:PRE-NamedEntityRecognition     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-NamedEntityRecognition     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-NamedEntityRecognition     arn:aws:lambda:ap-south-1:565803892007:function:PRE-NamedEntityRecognition     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-NamedEntityRecognition     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-NamedEntityRecognition     Video Classification - Use this task type when you need workers to classify videos using predefined labels that you specify. Workers are shown videos and are asked to choose one label for each video.    arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoMultiClass     arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoMultiClass     arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoMultiClass     arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoMultiClass     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoMultiClass     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoMultiClass     arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoMultiClass     arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoMultiClass     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoMultiClass     arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoMultiClass     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoMultiClass     arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoMultiClass     Video Frame Object Detection - Use this task type to have workers identify and locate objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to identify and localize various objects in a series of video frames, such as cars, bikes, and pedestrians.    arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectDetection     Video Frame Object Tracking - Use this task type to have workers track the movement of objects in a sequence of video frames (images extracted from a video) using bounding boxes. For example, you can use this task to ask workers to track the movement of objects, such as cars, bikes, and pedestrians.     arn:aws:lambda:us-east-1:432418664414:function:PRE-VideoObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:PRE-VideoObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:PRE-VideoObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:PRE-VideoObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VideoObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VideoObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:PRE-VideoObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:PRE-VideoObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VideoObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:PRE-VideoObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VideoObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:PRE-VideoObjectTracking     3D Point Cloud Modalities  Use the following pre-annotation lambdas for 3D point cloud labeling modality tasks. See 3D Point Cloud Task types  to learn more.   3D Point Cloud Object Detection - Use this task type when you want workers to classify objects in a 3D point cloud by drawing 3D cuboids around objects. For example, you can use this task type to ask workers to identify different types of objects in a point cloud, such as cars, bikes, and pedestrians.    arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectDetection     3D Point Cloud Object Tracking - Use this task type when you want workers to draw 3D cuboids around objects that appear in a sequence of 3D point cloud frames. For example, you can use this task type to ask workers to track the movement of vehicles across multiple point cloud frames.     arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudObjectTracking     3D Point Cloud Semantic Segmentation - Use this task type when you want workers to create a point-level semantic segmentation masks by painting objects in a 3D point cloud using different colors where each color is assigned to one of the classes you specify.    arn:aws:lambda:us-east-1:432418664414:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-3DPointCloudSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:PRE-3DPointCloudSemanticSegmentation     Use the following ARNs for Label Verification and Adjustment Jobs  Use label verification and adjustment jobs to review and adjust labels. To learn more, see Verify and Adjust Labels .  Bounding box verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgement for bounding box labels based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationBoundingBox     arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationBoundingBox     arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationBoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationBoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationBoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationBoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationBoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationBoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationBoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationBoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationBoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationBoundingBox     Bounding box adjustment - Finds the most similar boxes from different workers based on the Jaccard index of the adjusted annotations.    arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentBoundingBox     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentBoundingBox     Semantic segmentation verification - Uses a variant of the Expectation Maximization approach to estimate the true class of verification judgment for semantic segmentation labels based on annotations from individual workers.    arn:aws:lambda:us-east-1:432418664414:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-VerificationSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-VerificationSemanticSegmentation     Semantic segmentation adjustment - Treats each pixel in an image as a multi-class classification and treats pixel adjusted annotations from workers as "votes" for the correct label.    arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentSemanticSegmentation     Video Frame Object Detection Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to classify and localize objects in a sequence of video frames.    arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectDetection     Video Frame Object Tracking Adjustment - Use this task type when you want workers to adjust bounding boxes that workers have added to video frames to track object movement across a sequence of video frames.    arn:aws:lambda:us-east-1:432418664414:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-AdjustmentVideoObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:PRE-AdjustmentVideoObjectTracking     3D point cloud object detection adjustment - Adjust 3D cuboids in a point cloud frame.     arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectDetection     arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectDetection     3D point cloud object tracking adjustment - Adjust 3D cuboids across a sequence of point cloud frames.     arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudObjectTracking     arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudObjectTracking     3D point cloud semantic segmentation adjustment - Adjust semantic segmentation masks in a 3D point cloud.     arn:aws:lambda:us-east-1:432418664414:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:us-east-2:266458841044:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:us-west-2:081040173940:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-1:568282634449:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-1:477331159723:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-2:454466003867:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-south-1:565803892007:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-central-1:203001061592:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-northeast-2:845288260483:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:eu-west-2:487402164563:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ap-southeast-1:377565633583:function:PRE-Adjustment3DPointCloudSemanticSegmentation     arn:aws:lambda:ca-central-1:918755190332:function:PRE-Adjustment3DPointCloudSemanticSegmentation   
     */
    PreHumanTaskLambdaArn: LambdaFunctionArn;
    /**
     * Keywords used to describe the task so that workers on Amazon Mechanical Turk can discover the task.
     */
    TaskKeywords?: TaskKeywords;
    /**
     * A title for the task for your human workers.
     */
    TaskTitle: TaskTitle;
    /**
     * A description of the task for your human workers.
     */
    TaskDescription: TaskDescription;
    /**
     * The number of human workers that will label an object. 
     */
    NumberOfHumanWorkersPerDataObject: NumberOfHumanWorkersPerDataObject;
    /**
     * The amount of time that a worker has to complete a task.  If you create a custom labeling job, the maximum value for this parameter is 8 hours (28,800 seconds). If you create a labeling job using a built-in task type the maximum for this parameter depends on the task type you use:   For image and text labeling jobs, the maximum is 8 hours (28,800 seconds).   For 3D point cloud and video frame labeling jobs, the maximum is 30 days (2952,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.  
     */
    TaskTimeLimitInSeconds: TaskTimeLimitInSeconds;
    /**
     * The length of time that a task remains available for labeling by human workers. The default and maximum values for this parameter depend on the type of workforce you use.   If you choose the Amazon Mechanical Turk workforce, the maximum is 12 hours (43,200 seconds). The default is 6 hours (21,600 seconds).   If you choose a private or vendor workforce, the default value is 30 days (2592,000 seconds) for non-AL mode. For most users, the maximum is also 30 days.  
     */
    TaskAvailabilityLifetimeInSeconds?: TaskAvailabilityLifetimeInSeconds;
    /**
     * Defines the maximum number of data objects that can be labeled by human workers at the same time. Also referred to as batch size. Each object may have more than one worker at one time. The default value is 1000 objects. To increase the maximum value to 5000 objects, contact Amazon Web Services Support.
     */
    MaxConcurrentTaskCount?: MaxConcurrentTaskCount;
    /**
     * Configures how labels are consolidated across human workers.
     */
    AnnotationConsolidationConfig: AnnotationConsolidationConfig;
    /**
     * The price that you pay for each task performed by an Amazon Mechanical Turk worker.
     */
    PublicWorkforceTaskPrice?: PublicWorkforceTaskPrice;
  }
  export type HumanTaskUiArn = string;
  export type HumanTaskUiName = string;
  export type HumanTaskUiStatus = "Active"|"Deleting"|string;
  export type HumanTaskUiSummaries = HumanTaskUiSummary[];
  export interface HumanTaskUiSummary {
    /**
     * The name of the human task user interface.
     */
    HumanTaskUiName: HumanTaskUiName;
    /**
     * The Amazon Resource Name (ARN) of the human task user interface.
     */
    HumanTaskUiArn: HumanTaskUiArn;
    /**
     * A timestamp when SageMaker created the human task user interface.
     */
    CreationTime: Timestamp;
  }
  export interface HyperParameterAlgorithmSpecification {
    /**
     *  The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see Algorithms Provided by Amazon SageMaker: Common Parameters. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
     */
    TrainingImage?: AlgorithmImage;
    TrainingInputMode: TrainingInputMode;
    /**
     * The name of the resource algorithm to use for the hyperparameter tuning job. If you specify a value for this parameter, do not specify a value for TrainingImage.
     */
    AlgorithmName?: ArnOrName;
    /**
     * An array of MetricDefinition objects that specify the metrics that the algorithm emits.
     */
    MetricDefinitions?: MetricDefinitionList;
  }
  export type HyperParameterKey = string;
  export type HyperParameterScalingType = "Auto"|"Linear"|"Logarithmic"|"ReverseLogarithmic"|string;
  export interface HyperParameterSpecification {
    /**
     * The name of this hyperparameter. The name must be unique.
     */
    Name: ParameterName;
    /**
     * A brief description of the hyperparameter.
     */
    Description?: EntityDescription;
    /**
     * The type of this hyperparameter. The valid types are Integer, Continuous, Categorical, and FreeText.
     */
    Type: ParameterType;
    /**
     * The allowed range for this hyperparameter.
     */
    Range?: ParameterRange;
    /**
     * Indicates whether this hyperparameter is tunable in a hyperparameter tuning job.
     */
    IsTunable?: Boolean;
    /**
     * Indicates whether this hyperparameter is required.
     */
    IsRequired?: Boolean;
    /**
     * The default value for this hyperparameter. If a default value is specified, a hyperparameter cannot be required.
     */
    DefaultValue?: HyperParameterValue;
  }
  export type HyperParameterSpecifications = HyperParameterSpecification[];
  export interface HyperParameterTrainingJobDefinition {
    /**
     * The job definition name.
     */
    DefinitionName?: HyperParameterTrainingJobDefinitionName;
    TuningObjective?: HyperParameterTuningJobObjective;
    HyperParameterRanges?: ParameterRanges;
    /**
     * Specifies the values of hyperparameters that do not change for the tuning job.
     */
    StaticHyperParameters?: HyperParameters;
    /**
     * The HyperParameterAlgorithmSpecification object that specifies the resource algorithm to use for the training jobs that the tuning job launches.
     */
    AlgorithmSpecification: HyperParameterAlgorithmSpecification;
    /**
     * The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.
     */
    RoleArn: RoleArn;
    /**
     * An array of Channel objects that specify the input for the training jobs that the tuning job launches.
     */
    InputDataConfig?: InputDataConfig;
    /**
     * The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: VpcConfig;
    /**
     * Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.
     */
    OutputDataConfig: OutputDataConfig;
    /**
     * The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches. Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.  If you want to use hyperparameter optimization with instance type flexibility, use HyperParameterTuningResourceConfig instead. 
     */
    ResourceConfig?: ResourceConfig;
    /**
     * The configuration for the hyperparameter tuning resources, including the compute instances and storage volumes, used for training jobs launched by the tuning job. By default, storage volumes hold model artifacts and incremental states. Choose File for TrainingInputMode in the AlgorithmSpecification parameter to additionally store training data in the storage volume (optional).
     */
    HyperParameterTuningResourceConfig?: HyperParameterTuningResourceConfig;
    /**
     * Specifies a limit to how long a model hyperparameter training job can run. It also specifies how long a managed spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
     */
    StoppingCondition: StoppingCondition;
    /**
     * Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If network isolation is used for training jobs that are configured to use a VPC, SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * A Boolean indicating whether managed spot training is enabled (True) or not (False).
     */
    EnableManagedSpotTraining?: Boolean;
    CheckpointConfig?: CheckpointConfig;
    /**
     * The number of times to retry the job when the job fails due to an InternalServerError.
     */
    RetryStrategy?: RetryStrategy;
    /**
     * An environment variable that you can pass into the SageMaker CreateTrainingJob API. You can use an existing environment variable from the training container or use your own. See Define metrics and variables for more information.  The maximum number of items specified for Map Entries refers to the maximum number of environment variables for each TrainingJobDefinition and also the maximum for the hyperparameter tuning job itself. That is, the sum of the number of environment variables for all the training job definitions can't exceed the maximum number specified. 
     */
    Environment?: HyperParameterTrainingJobEnvironmentMap;
  }
  export type HyperParameterTrainingJobDefinitionName = string;
  export type HyperParameterTrainingJobDefinitions = HyperParameterTrainingJobDefinition[];
  export type HyperParameterTrainingJobEnvironmentKey = string;
  export type HyperParameterTrainingJobEnvironmentMap = {[key: string]: HyperParameterTrainingJobEnvironmentValue};
  export type HyperParameterTrainingJobEnvironmentValue = string;
  export type HyperParameterTrainingJobSummaries = HyperParameterTrainingJobSummary[];
  export interface HyperParameterTrainingJobSummary {
    /**
     * The training job definition name.
     */
    TrainingJobDefinitionName?: HyperParameterTrainingJobDefinitionName;
    /**
     * The name of the training job.
     */
    TrainingJobName: TrainingJobName;
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn: TrainingJobArn;
    /**
     * The HyperParameter tuning job that launched the training job.
     */
    TuningJobName?: HyperParameterTuningJobName;
    /**
     * The date and time that the training job was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the training job started.
     */
    TrainingStartTime?: Timestamp;
    /**
     * Specifies the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
     */
    TrainingEndTime?: Timestamp;
    /**
     * The status of the training job.
     */
    TrainingJobStatus: TrainingJobStatus;
    /**
     * A list of the hyperparameters for which you specified ranges to search.
     */
    TunedHyperParameters: HyperParameters;
    /**
     * The reason that the training job failed. 
     */
    FailureReason?: FailureReason;
    /**
     * The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.
     */
    FinalHyperParameterTuningJobObjectiveMetric?: FinalHyperParameterTuningJobObjectiveMetric;
    /**
     * The status of the objective metric for the training job:   Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.     Pending: The training job is in progress and evaluation of its final objective metric is pending.     Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.  
     */
    ObjectiveStatus?: ObjectiveStatus;
  }
  export type HyperParameterTuningAllocationStrategy = "Prioritized"|string;
  export interface HyperParameterTuningInstanceConfig {
    /**
     * The instance type used for processing of hyperparameter optimization jobs. Choose from general purpose (no GPUs) instance types: ml.m5.xlarge, ml.m5.2xlarge, and ml.m5.4xlarge or compute optimized (no GPUs) instance types: ml.c5.xlarge and ml.c5.2xlarge. For more information about instance types, see instance type descriptions.
     */
    InstanceType: TrainingInstanceType;
    /**
     * The number of instances of the type specified by InstanceType. Choose an instance count larger than 1 for distributed training algorithms. See Step 2: Launch a SageMaker Distributed Training Job Using the SageMaker Python SDK for more information.
     */
    InstanceCount: TrainingInstanceCount;
    /**
     * The volume size in GB of the data to be processed for hyperparameter optimization (optional).
     */
    VolumeSizeInGB: VolumeSizeInGB;
  }
  export type HyperParameterTuningInstanceConfigs = HyperParameterTuningInstanceConfig[];
  export type HyperParameterTuningJobArn = string;
  export interface HyperParameterTuningJobCompletionDetails {
    /**
     * The number of training jobs launched by a tuning job that are not improving (1% or less) as measured by model performance evaluated against an objective function.
     */
    NumberOfTrainingJobsObjectiveNotImproving?: Integer;
    /**
     * The time in timestamp format that AMT detected model convergence, as defined by a lack of significant improvement over time based on criteria developed over a wide range of diverse benchmarking tests.
     */
    ConvergenceDetectedTime?: Timestamp;
  }
  export interface HyperParameterTuningJobConfig {
    /**
     * Specifies how hyperparameter tuning chooses the combinations of hyperparameter values to use for the training job it launches. For information about search strategies, see How Hyperparameter Tuning Works.
     */
    Strategy: HyperParameterTuningJobStrategyType;
    /**
     * The configuration for the Hyperband optimization strategy. This parameter should be provided only if Hyperband is selected as the strategy for HyperParameterTuningJobConfig.
     */
    StrategyConfig?: HyperParameterTuningJobStrategyConfig;
    /**
     * The HyperParameterTuningJobObjective specifies the objective metric used to evaluate the performance of training jobs launched by this tuning job.
     */
    HyperParameterTuningJobObjective?: HyperParameterTuningJobObjective;
    /**
     * The ResourceLimits object that specifies the maximum number of training and parallel training jobs that can be used for this hyperparameter tuning job.
     */
    ResourceLimits: ResourceLimits;
    /**
     * The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches over to find the optimal configuration for the highest model performance against your chosen objective metric. 
     */
    ParameterRanges?: ParameterRanges;
    /**
     * Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. This parameter can take on one of the following values (the default value is OFF):  OFF  Training jobs launched by the hyperparameter tuning job do not use early stopping.  AUTO  SageMaker stops training jobs launched by the hyperparameter tuning job when they are unlikely to perform better than previously completed training jobs. For more information, see Stop Training Jobs Early.  
     */
    TrainingJobEarlyStoppingType?: TrainingJobEarlyStoppingType;
    /**
     * The tuning job's completion criteria.
     */
    TuningJobCompletionCriteria?: TuningJobCompletionCriteria;
    /**
     * A value used to initialize a pseudo-random number generator. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs.
     */
    RandomSeed?: RandomSeed;
  }
  export interface HyperParameterTuningJobConsumedResources {
    /**
     * The wall clock runtime in seconds used by your hyperparameter tuning job.
     */
    RuntimeInSeconds?: Integer;
  }
  export type HyperParameterTuningJobName = string;
  export interface HyperParameterTuningJobObjective {
    /**
     * Whether to minimize or maximize the objective metric.
     */
    Type: HyperParameterTuningJobObjectiveType;
    /**
     * The name of the metric to use for the objective metric.
     */
    MetricName: MetricName;
  }
  export type HyperParameterTuningJobObjectiveType = "Maximize"|"Minimize"|string;
  export type HyperParameterTuningJobObjectives = HyperParameterTuningJobObjective[];
  export interface HyperParameterTuningJobSearchEntity {
    /**
     * The name of a hyperparameter tuning job.
     */
    HyperParameterTuningJobName?: HyperParameterTuningJobName;
    /**
     * The Amazon Resource Name (ARN) of a hyperparameter tuning job.
     */
    HyperParameterTuningJobArn?: HyperParameterTuningJobArn;
    HyperParameterTuningJobConfig?: HyperParameterTuningJobConfig;
    TrainingJobDefinition?: HyperParameterTrainingJobDefinition;
    /**
     * The job definitions included in a hyperparameter tuning job.
     */
    TrainingJobDefinitions?: HyperParameterTrainingJobDefinitions;
    /**
     * The status of a hyperparameter tuning job.
     */
    HyperParameterTuningJobStatus?: HyperParameterTuningJobStatus;
    /**
     * The time that a hyperparameter tuning job was created.
     */
    CreationTime?: Timestamp;
    /**
     * The time that a hyperparameter tuning job ended.
     */
    HyperParameterTuningEndTime?: Timestamp;
    /**
     * The time that a hyperparameter tuning job was last modified.
     */
    LastModifiedTime?: Timestamp;
    TrainingJobStatusCounters?: TrainingJobStatusCounters;
    ObjectiveStatusCounters?: ObjectiveStatusCounters;
    BestTrainingJob?: HyperParameterTrainingJobSummary;
    OverallBestTrainingJob?: HyperParameterTrainingJobSummary;
    WarmStartConfig?: HyperParameterTuningJobWarmStartConfig;
    /**
     * The error that was created when a hyperparameter tuning job failed.
     */
    FailureReason?: FailureReason;
    /**
     * Information about either a current or completed hyperparameter tuning job.
     */
    TuningJobCompletionDetails?: HyperParameterTuningJobCompletionDetails;
    /**
     * The total amount of resources consumed by a hyperparameter tuning job.
     */
    ConsumedResources?: HyperParameterTuningJobConsumedResources;
    /**
     * The tags associated with a hyperparameter tuning job. For more information see Tagging Amazon Web Services resources.
     */
    Tags?: TagList;
  }
  export type HyperParameterTuningJobSortByOptions = "Name"|"Status"|"CreationTime"|string;
  export type HyperParameterTuningJobStatus = "Completed"|"InProgress"|"Failed"|"Stopped"|"Stopping"|"Deleting"|"DeleteFailed"|string;
  export interface HyperParameterTuningJobStrategyConfig {
    /**
     * The configuration for the object that specifies the Hyperband strategy. This parameter is only supported for the Hyperband selection for Strategy within the HyperParameterTuningJobConfig API.
     */
    HyperbandStrategyConfig?: HyperbandStrategyConfig;
  }
  export type HyperParameterTuningJobStrategyType = "Bayesian"|"Random"|"Hyperband"|"Grid"|string;
  export type HyperParameterTuningJobSummaries = HyperParameterTuningJobSummary[];
  export interface HyperParameterTuningJobSummary {
    /**
     * The name of the tuning job.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
    /**
     * The Amazon Resource Name (ARN) of the tuning job.
     */
    HyperParameterTuningJobArn: HyperParameterTuningJobArn;
    /**
     * The status of the tuning job.
     */
    HyperParameterTuningJobStatus: HyperParameterTuningJobStatus;
    /**
     * Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to evaluate at each iteration.
     */
    Strategy: HyperParameterTuningJobStrategyType;
    /**
     * The date and time that the tuning job was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the tuning job ended.
     */
    HyperParameterTuningEndTime?: Timestamp;
    /**
     * The date and time that the tuning job was modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.
     */
    TrainingJobStatusCounters: TrainingJobStatusCounters;
    /**
     * The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.
     */
    ObjectiveStatusCounters: ObjectiveStatusCounters;
    /**
     * The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.
     */
    ResourceLimits?: ResourceLimits;
  }
  export interface HyperParameterTuningJobWarmStartConfig {
    /**
     * An array of hyperparameter tuning jobs that are used as the starting point for the new hyperparameter tuning job. For more information about warm starting a hyperparameter tuning job, see Using a Previous Hyperparameter Tuning Job as a Starting Point. Hyperparameter tuning jobs created before October 1, 2018 cannot be used as parent jobs for warm start tuning jobs.
     */
    ParentHyperParameterTuningJobs: ParentHyperParameterTuningJobs;
    /**
     * Specifies one of the following:  IDENTICAL_DATA_AND_ALGORITHM  The new hyperparameter tuning job uses the same input data and training image as the parent tuning jobs. You can change the hyperparameter ranges to search and the maximum number of training jobs that the hyperparameter tuning job launches. You cannot use a new version of the training algorithm, unless the changes in the new version do not affect the algorithm itself. For example, changes that improve logging or adding support for a different data format are allowed. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.  TRANSFER_LEARNING  The new hyperparameter tuning job can include input data, hyperparameter ranges, maximum number of concurrent training jobs, and maximum number of training jobs that are different than those of its parent hyperparameter tuning jobs. The training image can also be a different version from the version used in the parent hyperparameter tuning job. You can also change hyperparameters from tunable to static, and from static to tunable, but the total number of static plus tunable hyperparameters must remain the same as it is in all parent jobs. The objective metric for the new tuning job must be the same as for all parent jobs.  
     */
    WarmStartType: HyperParameterTuningJobWarmStartType;
  }
  export type HyperParameterTuningJobWarmStartType = "IdenticalDataAndAlgorithm"|"TransferLearning"|string;
  export type HyperParameterTuningMaxRuntimeInSeconds = number;
  export interface HyperParameterTuningResourceConfig {
    /**
     * The instance type used to run hyperparameter optimization tuning jobs. See  descriptions of instance types for more information.
     */
    InstanceType?: TrainingInstanceType;
    /**
     * The number of compute instances of type InstanceType to use. For distributed training, select a value greater than 1.
     */
    InstanceCount?: TrainingInstanceCount;
    /**
     * The volume size in GB for the storage volume to be used in processing hyperparameter optimization jobs (optional). These volumes store model artifacts, incremental states and optionally, scratch space for training algorithms. Do not provide a value for this parameter if a value for InstanceConfigs is also specified. Some instance types have a fixed total local storage size. If you select one of these instances for training, VolumeSizeInGB cannot be greater than this total size. For a list of instance types with local instance storage and their sizes, see instance store volumes.  SageMaker supports only the General Purpose SSD (gp2) storage volume type. 
     */
    VolumeSizeInGB?: OptionalVolumeSizeInGB;
    /**
     * A key used by Amazon Web Services Key Management Service to encrypt data on the storage volume attached to the compute instances used to run the training job. You can use either of the following formats to specify a key. KMS Key ID:  "1234abcd-12ab-34cd-56ef-1234567890ab"  Amazon Resource Name (ARN) of a KMS key:  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"  Some instances use local storage, which use a hardware module to encrypt storage volumes. If you choose one of these instance types, you cannot request a VolumeKmsKeyId. For a list of instance types that use local storage, see instance store volumes. For more information about Amazon Web Services Key Management Service, see KMS encryption for more information.
     */
    VolumeKmsKeyId?: KmsKeyId;
    /**
     * The strategy that determines the order of preference for resources specified in InstanceConfigs used in hyperparameter optimization.
     */
    AllocationStrategy?: HyperParameterTuningAllocationStrategy;
    /**
     * A list containing the configuration(s) for one or more resources for processing hyperparameter jobs. These resources include compute instances and storage volumes to use in model training jobs launched by hyperparameter tuning jobs. The AllocationStrategy controls the order in which multiple configurations provided in InstanceConfigs are used.  If you only want to use a single instance configuration inside the HyperParameterTuningResourceConfig API, do not provide a value for InstanceConfigs. Instead, use InstanceType, VolumeSizeInGB and InstanceCount. If you use InstanceConfigs, do not provide values for InstanceType, VolumeSizeInGB or InstanceCount. 
     */
    InstanceConfigs?: HyperParameterTuningInstanceConfigs;
  }
  export type HyperParameterValue = string;
  export type HyperParameters = {[key: string]: HyperParameterValue};
  export interface HyperbandStrategyConfig {
    /**
     * The minimum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. If the value for MinResource has not been reached, the training job is not stopped by Hyperband.
     */
    MinResource?: HyperbandStrategyMinResource;
    /**
     * The maximum number of resources (such as epochs) that can be used by a training job launched by a hyperparameter tuning job. Once a job reaches the MaxResource value, it is stopped. If a value for MaxResource is not provided, and Hyperband is selected as the hyperparameter tuning strategy, HyperbandTraining attempts to infer MaxResource from the following keys (if present) in StaticsHyperParameters:    epochs     numepochs     n-epochs     n_epochs     num_epochs    If HyperbandStrategyConfig is unable to infer a value for MaxResource, it generates a validation error. The maximum value is 20,000 epochs. All metrics that correspond to an objective metric are used to derive early stopping decisions. For distributed training jobs, ensure that duplicate metrics are not printed in the logs across the individual nodes in a training job. If multiple nodes are publishing duplicate or incorrect metrics, training jobs may make an incorrect stopping decision and stop the job prematurely. 
     */
    MaxResource?: HyperbandStrategyMaxResource;
  }
  export type HyperbandStrategyMaxResource = number;
  export type HyperbandStrategyMinResource = number;
  export type IAMRoleArn = string;
  export interface IamIdentity {
    /**
     * The Amazon Resource Name (ARN) of the IAM identity.
     */
    Arn?: String;
    /**
     * The ID of the principal that assumes the IAM identity.
     */
    PrincipalId?: String;
    /**
     * The person or application which assumes the IAM identity.
     */
    SourceIdentity?: String;
  }
  export interface IamPolicyConstraints {
    /**
     * When SourceIp is Enabled the worker's IP address when a task is rendered in the worker portal is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. This IP address is checked by Amazon S3 and must match in order for the Amazon S3 resource to be rendered in the worker portal.
     */
    SourceIp?: EnabledOrDisabled;
    /**
     * When VpcSourceIp is Enabled the worker's IP address when a task is rendered in private worker portal inside the VPC is added to the IAM policy as a Condition used to generate the Amazon S3 presigned URL. To render the task successfully Amazon S3 checks that the presigned URL is being accessed over an Amazon S3 VPC Endpoint, and that the worker's IP address matches the IP address in the IAM policy. To learn more about configuring private worker portal, see Use Amazon VPC mode from a private worker portal.
     */
    VpcSourceIp?: EnabledOrDisabled;
  }
  export type IdempotencyToken = string;
  export interface IdentityProviderOAuthSetting {
    /**
     * The name of the data source that you're connecting to. Canvas currently supports OAuth for Snowflake and Salesforce Data Cloud.
     */
    DataSourceName?: DataSourceName;
    /**
     * Describes whether OAuth for a data source is enabled or disabled in the Canvas application.
     */
    Status?: FeatureStatus;
    /**
     * The ARN of an Amazon Web Services Secrets Manager secret that stores the credentials from your identity provider, such as the client ID and secret, authorization URL, and token URL. 
     */
    SecretArn?: SecretArn;
  }
  export type IdentityProviderOAuthSettings = IdentityProviderOAuthSetting[];
  export interface IdleSettings {
    /**
     * Indicates whether idle shutdown is activated for the application type.
     */
    LifecycleManagement?: LifecycleManagement;
    /**
     * The time that SageMaker waits after the application becomes idle before shutting it down.
     */
    IdleTimeoutInMinutes?: IdleTimeoutInMinutes;
    /**
     * The minimum value in minutes that custom idle shutdown can be set to by the user.
     */
    MinIdleTimeoutInMinutes?: IdleTimeoutInMinutes;
    /**
     * The maximum value in minutes that custom idle shutdown can be set to by the user.
     */
    MaxIdleTimeoutInMinutes?: IdleTimeoutInMinutes;
  }
  export type IdleTimeoutInMinutes = number;
  export interface Image {
    /**
     * When the image was created.
     */
    CreationTime: Timestamp;
    /**
     * The description of the image.
     */
    Description?: ImageDescription;
    /**
     * The name of the image as displayed.
     */
    DisplayName?: ImageDisplayName;
    /**
     * When a create, update, or delete operation fails, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * The ARN of the image.
     */
    ImageArn: ImageArn;
    /**
     * The name of the image.
     */
    ImageName: ImageName;
    /**
     * The status of the image.
     */
    ImageStatus: ImageStatus;
    /**
     * When the image was last modified.
     */
    LastModifiedTime: Timestamp;
  }
  export type ImageArn = string;
  export type ImageBaseImage = string;
  export interface ImageClassificationJobConfig {
    /**
     * How long a job is allowed to run, or how many candidates a job is allowed to generate.
     */
    CompletionCriteria?: AutoMLJobCompletionCriteria;
  }
  export interface ImageConfig {
    /**
     * Set this to one of the following values:    Platform - The model image is hosted in Amazon ECR.    Vpc - The model image is hosted in a private Docker registry in your VPC.  
     */
    RepositoryAccessMode: RepositoryAccessMode;
    /**
     * (Optional) Specifies an authentication configuration for the private docker registry where your model image is hosted. Specify a value for this property only if you specified Vpc as the value for the RepositoryAccessMode field, and the private Docker registry where the model image is hosted requires authentication.
     */
    RepositoryAuthConfig?: RepositoryAuthConfig;
  }
  export type ImageContainerImage = string;
  export type ImageDeleteProperty = string;
  export type ImageDeletePropertyList = ImageDeleteProperty[];
  export type ImageDescription = string;
  export type ImageDigest = string;
  export type ImageDisplayName = string;
  export type ImageName = string;
  export type ImageNameContains = string;
  export type ImageSortBy = "CREATION_TIME"|"LAST_MODIFIED_TIME"|"IMAGE_NAME"|string;
  export type ImageSortOrder = "ASCENDING"|"DESCENDING"|string;
  export type ImageStatus = "CREATING"|"CREATED"|"CREATE_FAILED"|"UPDATING"|"UPDATE_FAILED"|"DELETING"|"DELETE_FAILED"|string;
  export type ImageUri = string;
  export interface ImageVersion {
    /**
     * When the version was created.
     */
    CreationTime: Timestamp;
    /**
     * When a create or delete operation fails, the reason for the failure.
     */
    FailureReason?: FailureReason;
    /**
     * The ARN of the image the version is based on.
     */
    ImageArn: ImageArn;
    /**
     * The ARN of the version.
     */
    ImageVersionArn: ImageVersionArn;
    /**
     * The status of the version.
     */
    ImageVersionStatus: ImageVersionStatus;
    /**
     * When the version was last modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The version number.
     */
    Version: ImageVersionNumber;
  }
  export type ImageVersionAlias = string;
  export type ImageVersionArn = string;
  export type ImageVersionNumber = number;
  export type ImageVersionSortBy = "CREATION_TIME"|"LAST_MODIFIED_TIME"|"VERSION"|string;
  export type ImageVersionSortOrder = "ASCENDING"|"DESCENDING"|string;
  export type ImageVersionStatus = "CREATING"|"CREATED"|"CREATE_FAILED"|"DELETING"|"DELETE_FAILED"|string;
  export type ImageVersions = ImageVersion[];
  export type Images = Image[];
  export interface ImportHubContentRequest {
    /**
     * The name of the hub content to import.
     */
    HubContentName: HubContentName;
    /**
     * The version of the hub content to import.
     */
    HubContentVersion?: HubContentVersion;
    /**
     * The type of hub content to import.
     */
    HubContentType: HubContentType;
    /**
     * The version of the hub content schema to import.
     */
    DocumentSchemaVersion: DocumentSchemaVersion;
    /**
     * The name of the hub to import content into.
     */
    HubName: HubNameOrArn;
    /**
     * The display name of the hub content to import.
     */
    HubContentDisplayName?: HubContentDisplayName;
    /**
     * A description of the hub content to import.
     */
    HubContentDescription?: HubContentDescription;
    /**
     * A string that provides a description of the hub content. This string can include links, tables, and standard markdown formating.
     */
    HubContentMarkdown?: HubContentMarkdown;
    /**
     * The hub content document that describes information about the hub content such as type, associated containers, scripts, and more.
     */
    HubContentDocument: HubContentDocument;
    /**
     * The searchable keywords of the hub content.
     */
    HubContentSearchKeywords?: HubContentSearchKeywordList;
    /**
     * Any tags associated with the hub content.
     */
    Tags?: TagList;
  }
  export interface ImportHubContentResponse {
    /**
     * The ARN of the hub that the content was imported into.
     */
    HubArn: HubArn;
    /**
     * The ARN of the hub content that was imported.
     */
    HubContentArn: HubContentArn;
  }
  export type InferenceComponentArn = string;
  export interface InferenceComponentComputeResourceRequirements {
    /**
     * The number of CPU cores to allocate to run a model that you assign to an inference component.
     */
    NumberOfCpuCoresRequired?: NumberOfCpuCores;
    /**
     * The number of accelerators to allocate to run a model that you assign to an inference component. Accelerators include GPUs and Amazon Web Services Inferentia.
     */
    NumberOfAcceleratorDevicesRequired?: NumberOfAcceleratorDevices;
    /**
     * The minimum MB of memory to allocate to run a model that you assign to an inference component.
     */
    MinMemoryRequiredInMb: MemoryInMb;
    /**
     * The maximum MB of memory to allocate to run a model that you assign to an inference component.
     */
    MaxMemoryRequiredInMb?: MemoryInMb;
  }
  export interface InferenceComponentContainerSpecification {
    /**
     * The Amazon Elastic Container Registry (Amazon ECR) path where the Docker image for the model is stored.
     */
    Image?: ContainerImage;
    /**
     * The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
     */
    ArtifactUrl?: Url;
    /**
     * The environment variables to set in the Docker container. Each key and value in the Environment string-to-string map can have length of up to 1024. We support up to 16 entries in the map.
     */
    Environment?: EnvironmentMap;
  }
  export interface InferenceComponentContainerSpecificationSummary {
    DeployedImage?: DeployedImage;
    /**
     * The Amazon S3 path where the model artifacts are stored.
     */
    ArtifactUrl?: Url;
    /**
     * The environment variables to set in the Docker container.
     */
    Environment?: EnvironmentMap;
  }
  export type InferenceComponentCopyCount = number;
  export type InferenceComponentName = string;
  export type InferenceComponentNameContains = string;
  export interface InferenceComponentRuntimeConfig {
    /**
     * The number of runtime copies of the model container to deploy with the inference component. Each copy can serve inference requests.
     */
    CopyCount: InferenceComponentCopyCount;
  }
  export interface InferenceComponentRuntimeConfigSummary {
    /**
     * The number of runtime copies of the model container that you requested to deploy with the inference component.
     */
    DesiredCopyCount?: InferenceComponentCopyCount;
    /**
     * The number of runtime copies of the model container that are currently deployed.
     */
    CurrentCopyCount?: InferenceComponentCopyCount;
  }
  export type InferenceComponentSortKey = "Name"|"CreationTime"|"Status"|string;
  export interface InferenceComponentSpecification {
    /**
     * The name of an existing SageMaker model object in your account that you want to deploy with the inference component.
     */
    ModelName?: ModelName;
    /**
     * Defines a container that provides the runtime environment for a model that you deploy with an inference component.
     */
    Container?: InferenceComponentContainerSpecification;
    /**
     * Settings that take effect while the model container starts up.
     */
    StartupParameters?: InferenceComponentStartupParameters;
    /**
     * The compute resources allocated to run the model assigned to the inference component.
     */
    ComputeResourceRequirements: InferenceComponentComputeResourceRequirements;
  }
  export interface InferenceComponentSpecificationSummary {
    /**
     * The name of the SageMaker model object that is deployed with the inference component.
     */
    ModelName?: ModelName;
    /**
     * Details about the container that provides the runtime environment for the model that is deployed with the inference component.
     */
    Container?: InferenceComponentContainerSpecificationSummary;
    /**
     * Settings that take effect while the model container starts up.
     */
    StartupParameters?: InferenceComponentStartupParameters;
    /**
     * The compute resources allocated to run the model assigned to the inference component.
     */
    ComputeResourceRequirements?: InferenceComponentComputeResourceRequirements;
  }
  export interface InferenceComponentStartupParameters {
    /**
     * The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this inference component.
     */
    ModelDataDownloadTimeoutInSeconds?: ProductionVariantModelDataDownloadTimeoutInSeconds;
    /**
     * The timeout value, in seconds, for your inference container to pass health check by Amazon S3 Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
     */
    ContainerStartupHealthCheckTimeoutInSeconds?: ProductionVariantContainerStartupHealthCheckTimeoutInSeconds;
  }
  export type InferenceComponentStatus = "InService"|"Creating"|"Updating"|"Failed"|"Deleting"|string;
  export interface InferenceComponentSummary {
    /**
     * The time when the inference component was created.
     */
    CreationTime: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of the inference component.
     */
    InferenceComponentArn: InferenceComponentArn;
    /**
     * The name of the inference component.
     */
    InferenceComponentName: InferenceComponentName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint that hosts the inference component.
     */
    EndpointArn: EndpointArn;
    /**
     * The name of the endpoint that hosts the inference component.
     */
    EndpointName: EndpointName;
    /**
     * The name of the production variant that hosts the inference component.
     */
    VariantName: VariantName;
    /**
     * The status of the inference component.
     */
    InferenceComponentStatus?: InferenceComponentStatus;
    /**
     * The time when the inference component was last updated.
     */
    LastModifiedTime: Timestamp;
  }
  export type InferenceComponentSummaryList = InferenceComponentSummary[];
  export interface InferenceExecutionConfig {
    /**
     * How containers in a multi-container are run. The following values are valid.    SERIAL - Containers run as a serial pipeline.    DIRECT - Only the individual container that you specify is run.  
     */
    Mode: InferenceExecutionMode;
  }
  export type InferenceExecutionMode = "Serial"|"Direct"|string;
  export type InferenceExperimentArn = string;
  export interface InferenceExperimentDataStorageConfig {
    /**
     * The Amazon S3 bucket where the inference request and response data is stored. 
     */
    Destination: DestinationS3Uri;
    /**
     *  The Amazon Web Services Key Management Service key that Amazon SageMaker uses to encrypt captured data at rest using Amazon S3 server-side encryption. 
     */
    KmsKey?: KmsKeyId;
    ContentType?: CaptureContentTypeHeader;
  }
  export type InferenceExperimentDescription = string;
  export type InferenceExperimentList = InferenceExperimentSummary[];
  export type InferenceExperimentName = string;
  export interface InferenceExperimentSchedule {
    /**
     * The timestamp at which the inference experiment started or will start.
     */
    StartTime?: Timestamp;
    /**
     * The timestamp at which the inference experiment ended or will end.
     */
    EndTime?: Timestamp;
  }
  export type InferenceExperimentStatus = "Creating"|"Created"|"Updating"|"Running"|"Starting"|"Stopping"|"Completed"|"Cancelled"|string;
  export type InferenceExperimentStatusReason = string;
  export type InferenceExperimentStopDesiredState = "Completed"|"Cancelled"|string;
  export interface InferenceExperimentSummary {
    /**
     * The name of the inference experiment.
     */
    Name: InferenceExperimentName;
    /**
     * The type of the inference experiment.
     */
    Type: InferenceExperimentType;
    /**
     * The duration for which the inference experiment ran or will run. The maximum duration that you can set for an inference experiment is 30 days.
     */
    Schedule?: InferenceExperimentSchedule;
    /**
     * The status of the inference experiment.
     */
    Status: InferenceExperimentStatus;
    /**
     * The error message for the inference experiment status result.
     */
    StatusReason?: InferenceExperimentStatusReason;
    /**
     * The description of the inference experiment.
     */
    Description?: InferenceExperimentDescription;
    /**
     * The timestamp at which the inference experiment was created.
     */
    CreationTime: Timestamp;
    /**
     * The timestamp at which the inference experiment was completed.
     */
    CompletionTime?: Timestamp;
    /**
     * The timestamp when you last modified the inference experiment.
     */
    LastModifiedTime: Timestamp;
    /**
     *  The ARN of the IAM role that Amazon SageMaker can assume to access model artifacts and container images, and manage Amazon SageMaker Inference endpoints for model deployment. 
     */
    RoleArn?: RoleArn;
  }
  export type InferenceExperimentType = "ShadowMode"|string;
  export interface InferenceHubAccessConfig {
    /**
     * The ARN of the hub content for which deployment access is allowed.
     */
    HubContentArn: HubContentArn;
  }
  export type InferenceImage = string;
  export interface InferenceMetrics {
    /**
     * The expected maximum number of requests per minute for the instance.
     */
    MaxInvocations: Integer;
    /**
     * The expected model latency at maximum invocations per minute for the instance.
     */
    ModelLatency: Integer;
  }
  export interface InferenceRecommendation {
    /**
     * The recommendation ID which uniquely identifies each recommendation.
     */
    RecommendationId?: String;
    /**
     * The metrics used to decide what recommendation to make.
     */
    Metrics?: RecommendationMetrics;
    /**
     * Defines the endpoint configuration parameters.
     */
    EndpointConfiguration: EndpointOutputConfiguration;
    /**
     * Defines the model configuration.
     */
    ModelConfiguration: ModelConfiguration;
    /**
     * A timestamp that shows when the benchmark completed.
     */
    InvocationEndTime?: InvocationEndTime;
    /**
     * A timestamp that shows when the benchmark started.
     */
    InvocationStartTime?: InvocationStartTime;
  }
  export type InferenceRecommendations = InferenceRecommendation[];
  export interface InferenceRecommendationsJob {
    /**
     * The name of the job.
     */
    JobName: RecommendationJobName;
    /**
     * The job description.
     */
    JobDescription: RecommendationJobDescription;
    /**
     * The recommendation job type.
     */
    JobType: RecommendationJobType;
    /**
     * The Amazon Resource Name (ARN) of the recommendation job.
     */
    JobArn: RecommendationJobArn;
    /**
     * The status of the job.
     */
    Status: RecommendationJobStatus;
    /**
     * A timestamp that shows when the job was created.
     */
    CreationTime: CreationTime;
    /**
     * A timestamp that shows when the job completed.
     */
    CompletionTime?: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
    /**
     * A timestamp that shows when the job was last modified.
     */
    LastModifiedTime: LastModifiedTime;
    /**
     * If the job fails, provides information why the job failed.
     */
    FailureReason?: FailureReason;
    /**
     * The name of the created model.
     */
    ModelName?: ModelName;
    /**
     * The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
     */
    SamplePayloadUrl?: S3Uri;
    /**
     * The Amazon Resource Name (ARN) of a versioned model package.
     */
    ModelPackageVersionArn?: ModelPackageArn;
  }
  export interface InferenceRecommendationsJobStep {
    /**
     * The type of the subtask.  BENCHMARK: Evaluate the performance of your model on different instance types.
     */
    StepType: RecommendationStepType;
    /**
     * The name of the Inference Recommender job.
     */
    JobName: RecommendationJobName;
    /**
     * The current status of the benchmark.
     */
    Status: RecommendationJobStatus;
    /**
     * The details for a specific benchmark.
     */
    InferenceBenchmark?: RecommendationJobInferenceBenchmark;
  }
  export type InferenceRecommendationsJobSteps = InferenceRecommendationsJobStep[];
  export type InferenceRecommendationsJobs = InferenceRecommendationsJob[];
  export interface InferenceSpecification {
    /**
     * The Amazon ECR registry path of the Docker image that contains the inference code.
     */
    Containers: ModelPackageContainerDefinitionList;
    /**
     * A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed. This parameter is required for unversioned models, and optional for versioned models.
     */
    SupportedTransformInstanceTypes?: TransformInstanceTypes;
    /**
     * A list of the instance types that are used to generate inferences in real-time. This parameter is required for unversioned models, and optional for versioned models.
     */
    SupportedRealtimeInferenceInstanceTypes?: RealtimeInferenceInstanceTypes;
    /**
     * The supported MIME types for the input data.
     */
    SupportedContentTypes?: ContentTypes;
    /**
     * The supported MIME types for the output data.
     */
    SupportedResponseMIMETypes?: ResponseMIMETypes;
  }
  export type InferenceSpecificationName = string;
  export interface InfraCheckConfig {
    /**
     * Enables an infrastructure health check.
     */
    EnableInfraCheck?: EnableInfraCheck;
  }
  export type InitialInstanceCount = number;
  export type InitialNumberOfUsers = number;
  export type InitialTaskCount = number;
  export interface InputConfig {
    /**
     * The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
     */
    S3Uri: S3Uri;
    /**
     * Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.     TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.   Examples for one input:   If using the console, {"input":[1,1024,1024,3]}    If using the CLI, {\"input\":[1,1024,1024,3]}      Examples for two inputs:   If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}    If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}         KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.   Examples for one input:   If using the console, {"input_1":[1,3,224,224]}    If using the CLI, {\"input_1\":[1,3,224,224]}      Examples for two inputs:   If using the console, {"input_1": [1,3,224,224], "input_2":[1,3,224,224]}     If using the CLI, {\"input_1\": [1,3,224,224], \"input_2\":[1,3,224,224]}         MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.   Examples for one input:   If using the console, {"data":[1,3,1024,1024]}    If using the CLI, {\"data\":[1,3,1024,1024]}      Examples for two inputs:   If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}     If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}         PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.   Examples for one input in dictionary format:   If using the console, {"input0":[1,3,224,224]}    If using the CLI, {\"input0\":[1,3,224,224]}      Example for one input in list format: [[1,3,224,224]]    Examples for two inputs in dictionary format:   If using the console, {"input0":[1,3,224,224], "input1":[1,3,224,224]}    If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}       Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]       XGBOOST: input data name and shape are not needed.    DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):    shape: Input shape, for example {"input_1": {"shape": [1,224,224,3]}}. In addition to static input shapes, CoreML converter supports Flexible input shapes:   Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": ["1..10", 224, 224, 3]}}    Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}       default_shape: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": ["1..10", 224, 224, 3], "default_shape": [1, 224, 224, 3]}}     type: Input type. Allowed values: Image and Tensor. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale.    bias: If the input type is an Image, you need to provide the bias vector.    scale: If the input type is an Image, you need to provide a scale factor.   CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:   Tensor type input:    "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3]}}      Tensor type input without input name (PyTorch):    "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224]}]      Image type input:    "DataInputConfig": {"input_1": {"shape": [[1,224,224,3], [1,160,160,3]], "default_shape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}     "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}      Image type input without input name (PyTorch):    "DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "default_shape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]     "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}      Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice.   For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig. Specify the signature_def_key in  OutputConfig:CompilerOptions  if the model does not use TensorFlow's default signature def key. For example:    "DataInputConfig": {"inputs": [1, 224, 224, 3]}     "CompilerOptions": {"signature_def_key": "serving_custom"}      For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in  OutputConfig:CompilerOptions . For example:    "DataInputConfig": {"input_tensor:0": [1, 224, 224, 3]}     "CompilerOptions": {"output_names": ["output_tensor:0"]}     
     */
    DataInputConfig?: DataInputConfig;
    /**
     * Identifies the framework in which the model was trained. For example: TENSORFLOW.
     */
    Framework: Framework;
    /**
     * Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks. For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.
     */
    FrameworkVersion?: FrameworkVersion;
  }
  export type InputDataConfig = Channel[];
  export type InputMode = "Pipe"|"File"|string;
  export type InputModes = TrainingInputMode[];
  export interface InstanceGroup {
    /**
     * Specifies the instance type of the instance group.
     */
    InstanceType: TrainingInstanceType;
    /**
     * Specifies the number of instances of the instance group.
     */
    InstanceCount: TrainingInstanceCount;
    /**
     * Specifies the name of the instance group.
     */
    InstanceGroupName: InstanceGroupName;
  }
  export type InstanceGroupName = string;
  export type InstanceGroupNames = InstanceGroupName[];
  export type InstanceGroups = InstanceGroup[];
  export interface InstanceMetadataServiceConfiguration {
    /**
     * Indicates the minimum IMDS version that the notebook instance supports. When passed as part of CreateNotebookInstance, if no value is selected, then it defaults to IMDSv1. This means that both IMDSv1 and IMDSv2 are supported. If passed as part of UpdateNotebookInstance, there is no default.
     */
    MinimumInstanceMetadataServiceVersion: MinimumInstanceMetadataServiceVersion;
  }
  export type InstanceType = "ml.t2.medium"|"ml.t2.large"|"ml.t2.xlarge"|"ml.t2.2xlarge"|"ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.m5d.large"|"ml.m5d.xlarge"|"ml.m5d.2xlarge"|"ml.m5d.4xlarge"|"ml.m5d.8xlarge"|"ml.m5d.12xlarge"|"ml.m5d.16xlarge"|"ml.m5d.24xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.c5d.xlarge"|"ml.c5d.2xlarge"|"ml.c5d.4xlarge"|"ml.c5d.9xlarge"|"ml.c5d.18xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.p3dn.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.inf1.xlarge"|"ml.inf1.2xlarge"|"ml.inf1.6xlarge"|"ml.inf1.24xlarge"|"ml.p4d.24xlarge"|"ml.p4de.24xlarge"|"ml.p5.48xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c6i.large"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r6i.large"|"ml.r6i.xlarge"|"ml.r6i.2xlarge"|"ml.r6i.4xlarge"|"ml.r6i.8xlarge"|"ml.r6i.12xlarge"|"ml.r6i.16xlarge"|"ml.r6i.24xlarge"|"ml.r6i.32xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|"ml.m6id.large"|"ml.m6id.xlarge"|"ml.m6id.2xlarge"|"ml.m6id.4xlarge"|"ml.m6id.8xlarge"|"ml.m6id.12xlarge"|"ml.m6id.16xlarge"|"ml.m6id.24xlarge"|"ml.m6id.32xlarge"|"ml.c6id.large"|"ml.c6id.xlarge"|"ml.c6id.2xlarge"|"ml.c6id.4xlarge"|"ml.c6id.8xlarge"|"ml.c6id.12xlarge"|"ml.c6id.16xlarge"|"ml.c6id.24xlarge"|"ml.c6id.32xlarge"|"ml.r6id.large"|"ml.r6id.xlarge"|"ml.r6id.2xlarge"|"ml.r6id.4xlarge"|"ml.r6id.8xlarge"|"ml.r6id.12xlarge"|"ml.r6id.16xlarge"|"ml.r6id.24xlarge"|"ml.r6id.32xlarge"|"ml.g6.xlarge"|"ml.g6.2xlarge"|"ml.g6.4xlarge"|"ml.g6.8xlarge"|"ml.g6.12xlarge"|"ml.g6.16xlarge"|"ml.g6.24xlarge"|"ml.g6.48xlarge"|string;
  export type Integer = number;
  export interface IntegerParameterRange {
    /**
     * The name of the hyperparameter to search.
     */
    Name: ParameterKey;
    /**
     * The minimum value of the hyperparameter to search.
     */
    MinValue: ParameterValue;
    /**
     * The maximum value of the hyperparameter to search.
     */
    MaxValue: ParameterValue;
    /**
     * The scale that hyperparameter tuning uses to search the hyperparameter range. For information about choosing a hyperparameter scale, see Hyperparameter Scaling. One of the following values:  Auto  SageMaker hyperparameter tuning chooses the best scale for the hyperparameter.  Linear  Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale.  Logarithmic  Hyperparameter tuning searches the values in the hyperparameter range by using a logarithmic scale. Logarithmic scaling works only for ranges that have only values greater than 0.  
     */
    ScalingType?: HyperParameterScalingType;
  }
  export interface IntegerParameterRangeSpecification {
    /**
     * The minimum integer value allowed.
     */
    MinValue: ParameterValue;
    /**
     * The maximum integer value allowed.
     */
    MaxValue: ParameterValue;
  }
  export type IntegerParameterRanges = IntegerParameterRange[];
  export type InvocationEndTime = Date;
  export type InvocationStartTime = Date;
  export type InvocationsMaxRetries = number;
  export type InvocationsTimeoutInSeconds = number;
  export type IotRoleAlias = string;
  export type IsTrackingServerActive = "Active"|"Inactive"|string;
  export type ItemIdentifierAttributeName = string;
  export type JobDurationInSeconds = number;
  export type JobReferenceCode = string;
  export type JobReferenceCodeContains = string;
  export type JobType = "TRAINING"|"INFERENCE"|"NOTEBOOK_KERNEL"|string;
  export type JoinSource = "Input"|"None"|string;
  export type JsonContentType = string;
  export type JsonContentTypes = JsonContentType[];
  export type JsonPath = string;
  export interface JupyterLabAppImageConfig {
    FileSystemConfig?: FileSystemConfig;
    ContainerConfig?: ContainerConfig;
  }
  export interface JupyterLabAppSettings {
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A list of custom SageMaker images that are configured to run as a JupyterLab app.
     */
    CustomImages?: CustomImages;
    /**
     * The Amazon Resource Name (ARN) of the lifecycle configurations attached to the user profile or domain. To remove a lifecycle config, you must set LifecycleConfigArns to an empty list.
     */
    LifecycleConfigArns?: LifecycleConfigArns;
    /**
     * A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
     */
    CodeRepositories?: CodeRepositories;
    /**
     * Indicates whether idle shutdown is activated for JupyterLab applications.
     */
    AppLifecycleManagement?: AppLifecycleManagement;
    /**
     * The configuration parameters that specify the IAM roles assumed by the execution role of SageMaker (assumable roles) and the cluster instances or job execution environments (execution roles or runtime roles) to manage and access resources required for running Amazon EMR clusters or Amazon EMR Serverless applications.
     */
    EmrSettings?: EmrSettings;
  }
  export interface JupyterServerAppSettings {
    /**
     * The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the JupyterServer app. If you use the LifecycleConfigArns parameter, then this parameter is also required.
     */
    DefaultResourceSpec?: ResourceSpec;
    /**
     *  The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the JupyterServerApp. If you use this parameter, the DefaultResourceSpec parameter is also required.  To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list. 
     */
    LifecycleConfigArns?: LifecycleConfigArns;
    /**
     * A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterServer application.
     */
    CodeRepositories?: CodeRepositories;
  }
  export type KeepAlivePeriodInSeconds = number;
  export interface KendraSettings {
    /**
     * Describes whether the document querying feature is enabled or disabled in the Canvas application.
     */
    Status?: FeatureStatus;
  }
  export type KernelDisplayName = string;
  export interface KernelGatewayAppSettings {
    /**
     * The default instance type and the Amazon Resource Name (ARN) of the default SageMaker image used by the KernelGateway app.  The Amazon SageMaker Studio UI does not use the default instance type value set here. The default instance type set here is used when Apps are created using the CLI or CloudFormation and the instance type parameter value is not passed. 
     */
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A list of custom SageMaker images that are configured to run as a KernelGateway app.
     */
    CustomImages?: CustomImages;
    /**
     *  The Amazon Resource Name (ARN) of the Lifecycle Configurations attached to the the user profile or domain.  To remove a Lifecycle Config, you must set LifecycleConfigArns to an empty list. 
     */
    LifecycleConfigArns?: LifecycleConfigArns;
  }
  export interface KernelGatewayImageConfig {
    /**
     * The specification of the Jupyter kernels in the image.
     */
    KernelSpecs: KernelSpecs;
    /**
     * The Amazon Elastic File System storage configuration for a SageMaker image.
     */
    FileSystemConfig?: FileSystemConfig;
  }
  export type KernelName = string;
  export interface KernelSpec {
    /**
     * The name of the Jupyter kernel in the image. This value is case sensitive.
     */
    Name: KernelName;
    /**
     * The display name of the kernel.
     */
    DisplayName?: KernelDisplayName;
  }
  export type KernelSpecs = KernelSpec[];
  export type Key = string;
  export type KmsKeyId = string;
  export type LabelAttributeName = string;
  export type LabelCounter = number;
  export interface LabelCounters {
    /**
     * The total number of objects labeled.
     */
    TotalLabeled?: LabelCounter;
    /**
     * The total number of objects labeled by a human worker.
     */
    HumanLabeled?: LabelCounter;
    /**
     * The total number of objects labeled by automated data labeling.
     */
    MachineLabeled?: LabelCounter;
    /**
     * The total number of objects that could not be labeled due to an error.
     */
    FailedNonRetryableError?: LabelCounter;
    /**
     * The total number of objects not yet labeled.
     */
    Unlabeled?: LabelCounter;
  }
  export interface LabelCountersForWorkteam {
    /**
     * The total number of data objects labeled by a human worker.
     */
    HumanLabeled?: LabelCounter;
    /**
     * The total number of data objects that need to be labeled by a human worker.
     */
    PendingHuman?: LabelCounter;
    /**
     * The total number of tasks in the labeling job.
     */
    Total?: LabelCounter;
  }
  export type LabelingJobAlgorithmSpecificationArn = string;
  export interface LabelingJobAlgorithmsConfig {
    /**
     * Specifies the Amazon Resource Name (ARN) of the algorithm used for auto-labeling. You must select one of the following ARNs:    Image classification   arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/image-classification     Text classification   arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/text-classification     Object detection   arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/object-detection     Semantic Segmentation   arn:aws:sagemaker:region:027400017018:labeling-job-algorithm-specification/semantic-segmentation   
     */
    LabelingJobAlgorithmSpecificationArn: LabelingJobAlgorithmSpecificationArn;
    /**
     * At the end of an auto-label job Ground Truth sends the Amazon Resource Name (ARN) of the final model used for auto-labeling. You can use this model as the starting point for subsequent similar jobs by providing the ARN of the model here. 
     */
    InitialActiveLearningModelArn?: ModelArn;
    /**
     * Provides configuration information for a labeling job.
     */
    LabelingJobResourceConfig?: LabelingJobResourceConfig;
  }
  export type LabelingJobArn = string;
  export interface LabelingJobDataAttributes {
    /**
     * Declares that your content is free of personally identifiable information or adult content. SageMaker may restrict the Amazon Mechanical Turk workers that can view your task based on this information.
     */
    ContentClassifiers?: ContentClassifiers;
  }
  export interface LabelingJobDataSource {
    /**
     * The Amazon S3 location of the input data objects.
     */
    S3DataSource?: LabelingJobS3DataSource;
    /**
     * An Amazon SNS data source used for streaming labeling jobs. To learn more, see Send Data to a Streaming Labeling Job. 
     */
    SnsDataSource?: LabelingJobSnsDataSource;
  }
  export interface LabelingJobForWorkteamSummary {
    /**
     * The name of the labeling job that the work team is assigned to.
     */
    LabelingJobName?: LabelingJobName;
    /**
     * A unique identifier for a labeling job. You can use this to refer to a specific labeling job.
     */
    JobReferenceCode: JobReferenceCode;
    /**
     * The Amazon Web Services account ID of the account used to start the labeling job.
     */
    WorkRequesterAccountId: AccountId;
    /**
     * The date and time that the labeling job was created.
     */
    CreationTime: Timestamp;
    /**
     * Provides information about the progress of a labeling job.
     */
    LabelCounters?: LabelCountersForWorkteam;
    /**
     * The configured number of workers per data object.
     */
    NumberOfHumanWorkersPerDataObject?: NumberOfHumanWorkersPerDataObject;
  }
  export type LabelingJobForWorkteamSummaryList = LabelingJobForWorkteamSummary[];
  export interface LabelingJobInputConfig {
    /**
     * The location of the input data.
     */
    DataSource: LabelingJobDataSource;
    /**
     * Attributes of the data specified by the customer.
     */
    DataAttributes?: LabelingJobDataAttributes;
  }
  export type LabelingJobName = string;
  export interface LabelingJobOutput {
    /**
     * The Amazon S3 bucket location of the manifest file for labeled data. 
     */
    OutputDatasetS3Uri: S3Uri;
    /**
     * The Amazon Resource Name (ARN) for the most recent SageMaker model trained as part of automated data labeling. 
     */
    FinalActiveLearningModelArn?: ModelArn;
  }
  export interface LabelingJobOutputConfig {
    /**
     * The Amazon S3 location to write output data.
     */
    S3OutputPath: S3Uri;
    /**
     * The Amazon Web Services Key Management Service ID of the key used to encrypt the output data, if any. If you provide your own KMS key ID, you must add the required permissions to your KMS key described in Encrypt Output Data and Storage Volume with Amazon Web Services KMS. If you don't provide a KMS key ID, Amazon SageMaker uses the default Amazon Web Services KMS key for Amazon S3 for your role's account to encrypt your output data. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. 
     */
    KmsKeyId?: KmsKeyId;
    /**
     * An Amazon Simple Notification Service (Amazon SNS) output topic ARN. Provide a SnsTopicArn if you want to do real time chaining to another streaming job and receive an Amazon SNS notifications each time a data object is submitted by a worker. If you provide an SnsTopicArn in OutputConfig, when workers complete labeling tasks, Ground Truth will send labeling task output data to the SNS output topic you specify here.  To learn more, see Receive Output Data from a Streaming Labeling Job. 
     */
    SnsTopicArn?: SnsTopicArn;
  }
  export interface LabelingJobResourceConfig {
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training and inference jobs used for automated data labeling.  You can only specify a VolumeKmsKeyId when you create a labeling job with automated data labeling enabled using the API operation CreateLabelingJob. You cannot specify an Amazon Web Services KMS key to encrypt the storage volume used for automated data labeling model training and inference when you create a labeling job using the console. To learn more, see Output Data and Storage Volume Encryption. The VolumeKmsKeyId can be any of the following formats:   KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"   
     */
    VolumeKmsKeyId?: KmsKeyId;
    VpcConfig?: VpcConfig;
  }
  export interface LabelingJobS3DataSource {
    /**
     * The Amazon S3 location of the manifest file that describes the input data objects.  The input manifest file referenced in ManifestS3Uri must contain one of the following keys: source-ref or source. The value of the keys are interpreted as follows:    source-ref: The source of the object is the Amazon S3 object specified in the value. Use this value when the object is a binary object, such as an image.    source: The source of the object is the value. Use this value when the object is a text value.   If you are a new user of Ground Truth, it is recommended you review Use an Input Manifest File  in the Amazon SageMaker Developer Guide to learn how to create an input manifest file.
     */
    ManifestS3Uri: S3Uri;
  }
  export interface LabelingJobSnsDataSource {
    /**
     * The Amazon SNS input topic Amazon Resource Name (ARN). Specify the ARN of the input topic you will use to send new data objects to a streaming labeling job.
     */
    SnsTopicArn: SnsTopicArn;
  }
  export type LabelingJobStatus = "Initializing"|"InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export interface LabelingJobStoppingConditions {
    /**
     * The maximum number of objects that can be labeled by human workers.
     */
    MaxHumanLabeledObjectCount?: MaxHumanLabeledObjectCount;
    /**
     * The maximum number of input data objects that should be labeled.
     */
    MaxPercentageOfInputDatasetLabeled?: MaxPercentageOfInputDatasetLabeled;
  }
  export interface LabelingJobSummary {
    /**
     * The name of the labeling job.
     */
    LabelingJobName: LabelingJobName;
    /**
     * The Amazon Resource Name (ARN) assigned to the labeling job when it was created.
     */
    LabelingJobArn: LabelingJobArn;
    /**
     * The date and time that the job was created (timestamp).
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the job was last modified (timestamp).
     */
    LastModifiedTime: Timestamp;
    /**
     * The current status of the labeling job. 
     */
    LabelingJobStatus: LabelingJobStatus;
    /**
     * Counts showing the progress of the labeling job.
     */
    LabelCounters: LabelCounters;
    /**
     * The Amazon Resource Name (ARN) of the work team assigned to the job.
     */
    WorkteamArn: WorkteamArn;
    /**
     * The Amazon Resource Name (ARN) of a Lambda function. The function is run before each data object is sent to a worker.
     */
    PreHumanTaskLambdaArn: LambdaFunctionArn;
    /**
     * The Amazon Resource Name (ARN) of the Lambda function used to consolidate the annotations from individual workers into a label for a data object. For more information, see Annotation Consolidation.
     */
    AnnotationConsolidationLambdaArn?: LambdaFunctionArn;
    /**
     * If the LabelingJobStatus field is Failed, this field contains a description of the error.
     */
    FailureReason?: FailureReason;
    /**
     * The location of the output produced by the labeling job.
     */
    LabelingJobOutput?: LabelingJobOutput;
    /**
     * Input configuration for the labeling job.
     */
    InputConfig?: LabelingJobInputConfig;
  }
  export type LabelingJobSummaryList = LabelingJobSummary[];
  export type LambdaFunctionArn = string;
  export interface LambdaStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution.
     */
    Arn?: String256;
    /**
     * A list of the output parameters of the Lambda step.
     */
    OutputParameters?: OutputParameterList;
  }
  export type LandingUri = string;
  export type LastModifiedTime = Date;
  export interface LastUpdateStatus {
    /**
     * A value that indicates whether the update was made successful.
     */
    Status: LastUpdateStatusValue;
    /**
     * If the update wasn't successful, indicates the reason why it failed.
     */
    FailureReason?: FailureReason;
  }
  export type LastUpdateStatusValue = "Successful"|"Failed"|"InProgress"|string;
  export type LifecycleConfigArns = StudioLifecycleConfigArn[];
  export type LifecycleManagement = "ENABLED"|"DISABLED"|string;
  export type LineageEntityParameters = {[key: string]: StringParameterValue};
  export type LineageGroupArn = string;
  export type LineageGroupNameOrArn = string;
  export type LineageGroupSummaries = LineageGroupSummary[];
  export interface LineageGroupSummary {
    /**
     * The Amazon Resource Name (ARN) of the lineage group resource.
     */
    LineageGroupArn?: LineageGroupArn;
    /**
     * The name or Amazon Resource Name (ARN) of the lineage group.
     */
    LineageGroupName?: ExperimentEntityName;
    /**
     * The display name of the lineage group summary.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The creation time of the lineage group summary.
     */
    CreationTime?: Timestamp;
    /**
     * The last modified time of the lineage group summary.
     */
    LastModifiedTime?: Timestamp;
  }
  export type LineageType = "TrialComponent"|"Artifact"|"Context"|"Action"|string;
  export interface ListActionsRequest {
    /**
     * A filter that returns only actions with the specified source URI.
     */
    SourceUri?: SourceUri;
    /**
     * A filter that returns only actions of the specified type.
     */
    ActionType?: String256;
    /**
     * A filter that returns only actions created on or after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only actions created on or before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortActionsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous call to ListActions didn't return the full set of actions, the call returns a token for getting the next set of actions.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of actions to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListActionsResponse {
    /**
     * A list of actions and their properties.
     */
    ActionSummaries?: ActionSummaries;
    /**
     * A token for getting the next set of actions, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListAlgorithmsInput {
    /**
     * A filter that returns only algorithms created after the specified time (timestamp).
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only algorithms created before the specified time (timestamp).
     */
    CreationTimeBefore?: CreationTime;
    /**
     * The maximum number of algorithms to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * If the response to a previous ListAlgorithms request was truncated, the response includes a NextToken. To retrieve the next set of algorithms, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: AlgorithmSortBy;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListAlgorithmsOutput {
    /**
     * &gt;An array of AlgorithmSummary objects, each of which lists an algorithm.
     */
    AlgorithmSummaryList: AlgorithmSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListAliasesRequest {
    /**
     * The name of the image.
     */
    ImageName: ImageName;
    /**
     * The alias of the image version.
     */
    Alias?: SageMakerImageVersionAlias;
    /**
     * The version of the image. If image version is not specified, the aliases of all versions of the image are listed.
     */
    Version?: ImageVersionNumber;
    /**
     * The maximum number of aliases to return.
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListAliases didn't return the full set of aliases, the call returns a token for retrieving the next set of aliases.
     */
    NextToken?: NextToken;
  }
  export interface ListAliasesResponse {
    /**
     * A list of SageMaker image version aliases.
     */
    SageMakerImageVersionAliases?: SageMakerImageVersionAliases;
    /**
     * A token for getting the next set of aliases, if more aliases exist.
     */
    NextToken?: NextToken;
  }
  export interface ListAppImageConfigsRequest {
    /**
     * The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListImages didn't return the full set of AppImageConfigs, the call returns a token for getting the next set of AppImageConfigs.
     */
    NextToken?: NextToken;
    /**
     * A filter that returns only AppImageConfigs whose name contains the specified string.
     */
    NameContains?: AppImageConfigName;
    /**
     * A filter that returns only AppImageConfigs created on or before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only AppImageConfigs created on or after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only AppImageConfigs modified on or before the specified time.
     */
    ModifiedTimeBefore?: Timestamp;
    /**
     * A filter that returns only AppImageConfigs modified on or after the specified time.
     */
    ModifiedTimeAfter?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: AppImageConfigSortKey;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListAppImageConfigsResponse {
    /**
     * A token for getting the next set of AppImageConfigs, if there are any.
     */
    NextToken?: NextToken;
    /**
     * A list of AppImageConfigs and their properties.
     */
    AppImageConfigs?: AppImageConfigList;
  }
  export interface ListAppsRequest {
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
     */
    MaxResults?: MaxResults;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: AppSortKey;
    /**
     * A parameter to search for the domain ID.
     */
    DomainIdEquals?: DomainId;
    /**
     * A parameter to search by user profile name. If SpaceNameEquals is set, then this value cannot be set.
     */
    UserProfileNameEquals?: UserProfileName;
    /**
     * A parameter to search by space name. If UserProfileNameEquals is set, then this value cannot be set.
     */
    SpaceNameEquals?: SpaceName;
  }
  export interface ListAppsResponse {
    /**
     * The list of apps.
     */
    Apps?: AppList;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListArtifactsRequest {
    /**
     * A filter that returns only artifacts with the specified source URI.
     */
    SourceUri?: SourceUri;
    /**
     * A filter that returns only artifacts of the specified type.
     */
    ArtifactType?: String256;
    /**
     * A filter that returns only artifacts created on or after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only artifacts created on or before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortArtifactsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous call to ListArtifacts didn't return the full set of artifacts, the call returns a token for getting the next set of artifacts.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of artifacts to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListArtifactsResponse {
    /**
     * A list of artifacts and their properties.
     */
    ArtifactSummaries?: ArtifactSummaries;
    /**
     * A token for getting the next set of artifacts, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListAssociationsRequest {
    /**
     * A filter that returns only associations with the specified source ARN.
     */
    SourceArn?: AssociationEntityArn;
    /**
     * A filter that returns only associations with the specified destination Amazon Resource Name (ARN).
     */
    DestinationArn?: AssociationEntityArn;
    /**
     * A filter that returns only associations with the specified source type.
     */
    SourceType?: String256;
    /**
     * A filter that returns only associations with the specified destination type.
     */
    DestinationType?: String256;
    /**
     * A filter that returns only associations of the specified type.
     */
    AssociationType?: AssociationEdgeType;
    /**
     * A filter that returns only associations created on or after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only associations created on or before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortAssociationsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous call to ListAssociations didn't return the full set of associations, the call returns a token for getting the next set of associations.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of associations to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListAssociationsResponse {
    /**
     * A list of associations and their properties.
     */
    AssociationSummaries?: AssociationSummaries;
    /**
     * A token for getting the next set of associations, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListAutoMLJobsRequest {
    /**
     * Request a list of jobs, using a filter for time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Request a list of jobs, using a filter for time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Request a list of jobs, using a filter for time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Request a list of jobs, using a filter for time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Request a list of jobs, using a search filter for name.
     */
    NameContains?: AutoMLNameContains;
    /**
     * Request a list of jobs, using a filter for status.
     */
    StatusEquals?: AutoMLJobStatus;
    /**
     * The sort order for the results. The default is Descending.
     */
    SortOrder?: AutoMLSortOrder;
    /**
     * The parameter by which to sort the results. The default is Name.
     */
    SortBy?: AutoMLSortBy;
    /**
     * Request a list of jobs up to a specified limit.
     */
    MaxResults?: AutoMLMaxResults;
    /**
     * If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListAutoMLJobsResponse {
    /**
     * Returns a summary list of jobs.
     */
    AutoMLJobSummaries: AutoMLJobSummaries;
    /**
     * If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListCandidatesForAutoMLJobRequest {
    /**
     * List the candidates created for the job by providing the job's name.
     */
    AutoMLJobName: AutoMLJobName;
    /**
     * List the candidates for the job and filter by status.
     */
    StatusEquals?: CandidateStatus;
    /**
     * List the candidates for the job and filter by candidate name.
     */
    CandidateNameEquals?: CandidateName;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: AutoMLSortOrder;
    /**
     * The parameter by which to sort the results. The default is Descending.
     */
    SortBy?: CandidateSortBy;
    /**
     * List the job's candidates up to a specified limit.
     */
    MaxResults?: AutoMLMaxResultsForTrials;
    /**
     * If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListCandidatesForAutoMLJobResponse {
    /**
     * Summaries about the AutoMLCandidates.
     */
    Candidates: AutoMLCandidates;
    /**
     * If the previous response was truncated, you receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListClusterNodesRequest {
    /**
     * The string name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster in which you want to retrieve the list of nodes.
     */
    ClusterName: ClusterNameOrArn;
    /**
     * A filter that returns nodes in a SageMaker HyperPod cluster created after the specified time. Timestamps are formatted according to the ISO 8601 standard.  Acceptable formats include:    YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z     YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00     YYYY-MM-DD, for example, 2014-10-01    Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.   For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns the instance groups whose name contain a specified string.
     */
    InstanceGroupNameContains?: ClusterInstanceGroupName;
    /**
     * The maximum number of nodes to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * If the result of the previous ListClusterNodes request was truncated, the response includes a NextToken. To retrieve the next set of cluster nodes, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The field by which to sort results. The default value is CREATION_TIME.
     */
    SortBy?: ClusterSortBy;
    /**
     * The sort order for results. The default value is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListClusterNodesResponse {
    /**
     * The next token specified for listing instances in a SageMaker HyperPod cluster.
     */
    NextToken: NextToken;
    /**
     * The summaries of listed instances in a SageMaker HyperPod cluster
     */
    ClusterNodeSummaries: ClusterNodeSummaries;
  }
  export interface ListClustersRequest {
    /**
     * Set a start time for the time range during which you want to list SageMaker HyperPod clusters. Timestamps are formatted according to the ISO 8601 standard.  Acceptable formats include:    YYYY-MM-DDThh:mm:ss.sssTZD (UTC), for example, 2014-10-01T20:30:00.000Z     YYYY-MM-DDThh:mm:ss.sssTZD (with offset), for example, 2014-10-01T12:30:00.000-08:00     YYYY-MM-DD, for example, 2014-10-01    Unix time in seconds, for example, 1412195400. This is also referred to as Unix Epoch time and represents the number of seconds since midnight, January 1, 1970 UTC.   For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Set an end time for the time range during which you want to list SageMaker HyperPod clusters. A filter that returns nodes in a SageMaker HyperPod cluster created before the specified time. The acceptable formats are the same as the timestamp formats for CreationTimeAfter. For more information about the timestamp format, see Timestamp in the Amazon Web Services Command Line Interface User Guide.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Set the maximum number of SageMaker HyperPod clusters to list.
     */
    MaxResults?: MaxResults;
    /**
     * Set the maximum number of instances to print in the list.
     */
    NameContains?: NameContains;
    /**
     * Set the next token to retrieve the list of SageMaker HyperPod clusters.
     */
    NextToken?: NextToken;
    /**
     * The field by which to sort results. The default value is CREATION_TIME.
     */
    SortBy?: ClusterSortBy;
    /**
     * The sort order for results. The default value is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListClustersResponse {
    /**
     * If the result of the previous ListClusters request was truncated, the response includes a NextToken. To retrieve the next set of clusters, use the token in the next request.
     */
    NextToken: NextToken;
    /**
     * The summaries of listed SageMaker HyperPod clusters.
     */
    ClusterSummaries: ClusterSummaries;
  }
  export interface ListCodeRepositoriesInput {
    /**
     * A filter that returns only Git repositories that were created after the specified time.
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only Git repositories that were created before the specified time.
     */
    CreationTimeBefore?: CreationTime;
    /**
     * A filter that returns only Git repositories that were last modified after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only Git repositories that were last modified before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * The maximum number of Git repositories to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.
     */
    NameContains?: CodeRepositoryNameContains;
    /**
     * If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The field to sort results by. The default is Name.
     */
    SortBy?: CodeRepositorySortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: CodeRepositorySortOrder;
  }
  export interface ListCodeRepositoriesOutput {
    /**
     * Gets a list of summaries of the Git repositories. Each summary specifies the following values for the repository:    Name   Amazon Resource Name (ARN)   Creation time   Last modified time   Configuration information, including the URL location of the repository and the ARN of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository.  
     */
    CodeRepositorySummaryList: CodeRepositorySummaryList;
    /**
     * If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListCompilationJobsRequest {
    /**
     * If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of model compilation jobs to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns the model compilation jobs that were created after a specified time. 
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns the model compilation jobs that were created before a specified time.
     */
    CreationTimeBefore?: CreationTime;
    /**
     * A filter that returns the model compilation jobs that were modified after a specified time.
     */
    LastModifiedTimeAfter?: LastModifiedTime;
    /**
     * A filter that returns the model compilation jobs that were modified before a specified time.
     */
    LastModifiedTimeBefore?: LastModifiedTime;
    /**
     * A filter that returns the model compilation jobs whose name contains a specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that retrieves model compilation jobs with a specific CompilationJobStatus status.
     */
    StatusEquals?: CompilationJobStatus;
    /**
     * The field by which to sort results. The default is CreationTime.
     */
    SortBy?: ListCompilationJobsSortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListCompilationJobsResponse {
    /**
     * An array of CompilationJobSummary objects, each describing a model compilation job. 
     */
    CompilationJobSummaries: CompilationJobSummaries;
    /**
     * If the response is truncated, Amazon SageMaker returns this NextToken. To retrieve the next set of model compilation jobs, use this token in the next request.
     */
    NextToken?: NextToken;
  }
  export type ListCompilationJobsSortBy = "Name"|"CreationTime"|"Status"|string;
  export interface ListContextsRequest {
    /**
     * A filter that returns only contexts with the specified source URI.
     */
    SourceUri?: SourceUri;
    /**
     * A filter that returns only contexts of the specified type.
     */
    ContextType?: String256;
    /**
     * A filter that returns only contexts created on or after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only contexts created on or before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortContextsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous call to ListContexts didn't return the full set of contexts, the call returns a token for getting the next set of contexts.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of contexts to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListContextsResponse {
    /**
     * A list of contexts and their properties.
     */
    ContextSummaries?: ContextSummaries;
    /**
     * A token for getting the next set of contexts, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListDataQualityJobDefinitionsRequest {
    /**
     * A filter that lists the data quality job definitions associated with the specified endpoint.
     */
    EndpointName?: EndpointName;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: MonitoringJobDefinitionSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.&gt;
     */
    NextToken?: NextToken;
    /**
     * The maximum number of data quality monitoring job definitions to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the data quality monitoring job definition name. This filter returns only data quality monitoring job definitions whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only data quality monitoring job definitions created before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only data quality monitoring job definitions created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListDataQualityJobDefinitionsResponse {
    /**
     * A list of data quality monitoring job definitions.
     */
    JobDefinitionSummaries: MonitoringJobDefinitionSummaryList;
    /**
     * If the result of the previous ListDataQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of data quality monitoring job definitions, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListDeviceFleetsRequest {
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to select.
     */
    MaxResults?: ListMaxResults;
    /**
     * Filter fleets where packaging job was created after specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Filter fleets where the edge packaging job was created before specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Select fleets where the job was updated after X
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Select fleets where the job was updated before X
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Filter for fleets containing this name in their fleet device name.
     */
    NameContains?: NameContains;
    /**
     * The column to sort by.
     */
    SortBy?: ListDeviceFleetsSortBy;
    /**
     * What direction to sort in.
     */
    SortOrder?: SortOrder;
  }
  export interface ListDeviceFleetsResponse {
    /**
     * Summary of the device fleet.
     */
    DeviceFleetSummaries: DeviceFleetSummaries;
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
  }
  export type ListDeviceFleetsSortBy = "NAME"|"CREATION_TIME"|"LAST_MODIFIED_TIME"|string;
  export interface ListDevicesRequest {
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
    /**
     * Maximum number of results to select.
     */
    MaxResults?: ListMaxResults;
    /**
     * Select fleets where the job was updated after X
     */
    LatestHeartbeatAfter?: Timestamp;
    /**
     * A filter that searches devices that contains this name in any of their models.
     */
    ModelName?: EntityName;
    /**
     * Filter for fleets containing this name in their device fleet name.
     */
    DeviceFleetName?: EntityName;
  }
  export interface ListDevicesResponse {
    /**
     * Summary of devices.
     */
    DeviceSummaries: DeviceSummaries;
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
  }
  export interface ListDomainsRequest {
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListDomainsResponse {
    /**
     * The list of domains.
     */
    Domains?: DomainList;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListEdgeDeploymentPlansRequest {
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to select (50 by default).
     */
    MaxResults?: ListMaxResults;
    /**
     * Selects edge deployment plans created after this time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Selects edge deployment plans created before this time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Selects edge deployment plans that were last updated after this time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Selects edge deployment plans that were last updated before this time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Selects edge deployment plans with names containing this name.
     */
    NameContains?: NameContains;
    /**
     * Selects edge deployment plans with a device fleet name containing this name.
     */
    DeviceFleetNameContains?: NameContains;
    /**
     * The column by which to sort the edge deployment plans. Can be one of NAME, DEVICEFLEETNAME, CREATIONTIME, LASTMODIFIEDTIME.
     */
    SortBy?: ListEdgeDeploymentPlansSortBy;
    /**
     * The direction of the sorting (ascending or descending).
     */
    SortOrder?: SortOrder;
  }
  export interface ListEdgeDeploymentPlansResponse {
    /**
     * List of summaries of edge deployment plans.
     */
    EdgeDeploymentPlanSummaries: EdgeDeploymentPlanSummaries;
    /**
     * The token to use when calling the next page of results.
     */
    NextToken?: NextToken;
  }
  export type ListEdgeDeploymentPlansSortBy = "NAME"|"DEVICE_FLEET_NAME"|"CREATION_TIME"|"LAST_MODIFIED_TIME"|string;
  export interface ListEdgePackagingJobsRequest {
    /**
     * The response from the last list when returning a list large enough to need tokening.
     */
    NextToken?: NextToken;
    /**
     * Maximum number of results to select.
     */
    MaxResults?: ListMaxResults;
    /**
     * Select jobs where the job was created after specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Select jobs where the job was created before specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Select jobs where the job was updated after specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Select jobs where the job was updated before specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Filter for jobs containing this name in their packaging job name.
     */
    NameContains?: NameContains;
    /**
     * Filter for jobs where the model name contains this string.
     */
    ModelNameContains?: NameContains;
    /**
     * The job status to filter for.
     */
    StatusEquals?: EdgePackagingJobStatus;
    /**
     * Use to specify what column to sort by.
     */
    SortBy?: ListEdgePackagingJobsSortBy;
    /**
     * What direction to sort by.
     */
    SortOrder?: SortOrder;
  }
  export interface ListEdgePackagingJobsResponse {
    /**
     * Summaries of edge packaging jobs.
     */
    EdgePackagingJobSummaries: EdgePackagingJobSummaries;
    /**
     * Token to use when calling the next page of results.
     */
    NextToken?: NextToken;
  }
  export type ListEdgePackagingJobsSortBy = "NAME"|"MODEL_NAME"|"CREATION_TIME"|"LAST_MODIFIED_TIME"|"STATUS"|string;
  export interface ListEndpointConfigsInput {
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: EndpointConfigSortKey;
    /**
     * The sort order for results. The default is Descending.
     */
    SortOrder?: OrderKey;
    /**
     * If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request. 
     */
    NextToken?: PaginationToken;
    /**
     * The maximum number of training jobs to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string. 
     */
    NameContains?: EndpointConfigNameContains;
    /**
     * A filter that returns only endpoint configurations created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListEndpointConfigsOutput {
    /**
     * An array of endpoint configurations.
     */
    EndpointConfigs: EndpointConfigSummaryList;
    /**
     *  If the response is truncated, SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request 
     */
    NextToken?: PaginationToken;
  }
  export interface ListEndpointsInput {
    /**
     * Sorts the list of results. The default is CreationTime.
     */
    SortBy?: EndpointSortKey;
    /**
     * The sort order for results. The default is Descending.
     */
    SortOrder?: OrderKey;
    /**
     * If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.
     */
    NextToken?: PaginationToken;
    /**
     * The maximum number of endpoints to return in the response. This value defaults to 10.
     */
    MaxResults?: MaxResults;
    /**
     * A string in endpoint names. This filter returns only endpoints whose name contains the specified string.
     */
    NameContains?: EndpointNameContains;
    /**
     * A filter that returns only endpoints that were created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
    /**
     *  A filter that returns only endpoints that were modified before the specified timestamp. 
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     *  A filter that returns only endpoints that were modified after the specified timestamp. 
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     *  A filter that returns only endpoints with the specified status.
     */
    StatusEquals?: EndpointStatus;
  }
  export interface ListEndpointsOutput {
    /**
     *  An array or endpoint objects. 
     */
    Endpoints: EndpointSummaryList;
    /**
     *  If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request. 
     */
    NextToken?: PaginationToken;
  }
  export interface ListExperimentsRequest {
    /**
     * A filter that returns only experiments created after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only experiments created before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortExperimentsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous call to ListExperiments didn't return the full set of experiments, the call returns a token for getting the next set of experiments.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of experiments to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListExperimentsResponse {
    /**
     * A list of the summaries of your experiments.
     */
    ExperimentSummaries?: ExperimentSummaries;
    /**
     * A token for getting the next set of experiments, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListFeatureGroupsRequest {
    /**
     * A string that partially matches one or more FeatureGroups names. Filters FeatureGroups by name. 
     */
    NameContains?: FeatureGroupNameContains;
    /**
     * A FeatureGroup status. Filters by FeatureGroup status. 
     */
    FeatureGroupStatusEquals?: FeatureGroupStatus;
    /**
     * An OfflineStore status. Filters by OfflineStore status. 
     */
    OfflineStoreStatusEquals?: OfflineStoreStatusValue;
    /**
     * Use this parameter to search for FeatureGroupss created after a specific date and time.
     */
    CreationTimeAfter?: CreationTime;
    /**
     * Use this parameter to search for FeatureGroupss created before a specific date and time.
     */
    CreationTimeBefore?: CreationTime;
    /**
     * The order in which feature groups are listed.
     */
    SortOrder?: FeatureGroupSortOrder;
    /**
     * The value on which the feature group list is sorted.
     */
    SortBy?: FeatureGroupSortBy;
    /**
     * The maximum number of results returned by ListFeatureGroups.
     */
    MaxResults?: FeatureGroupMaxResults;
    /**
     * A token to resume pagination of ListFeatureGroups results.
     */
    NextToken?: NextToken;
  }
  export interface ListFeatureGroupsResponse {
    /**
     * A summary of feature groups.
     */
    FeatureGroupSummaries: FeatureGroupSummaries;
    /**
     * A token to resume pagination of ListFeatureGroups results.
     */
    NextToken?: NextToken;
  }
  export interface ListFlowDefinitionsRequest {
    /**
     * A filter that returns only flow definitions with a creation time greater than or equal to the specified timestamp.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only flow definitions that were created before the specified timestamp.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * An optional value that specifies whether you want the results sorted in Ascending or Descending order.
     */
    SortOrder?: SortOrder;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
    /**
     * The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
     */
    MaxResults?: MaxResults;
  }
  export interface ListFlowDefinitionsResponse {
    /**
     * An array of objects describing the flow definitions.
     */
    FlowDefinitionSummaries: FlowDefinitionSummaries;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
  }
  export interface ListHubContentVersionsRequest {
    /**
     * The name of the hub to list the content versions of.
     */
    HubName: HubNameOrArn;
    /**
     * The type of hub content to list versions of.
     */
    HubContentType: HubContentType;
    /**
     * The name of the hub content.
     */
    HubContentName: HubContentName;
    /**
     * The lower bound of the hub content versions to list.
     */
    MinVersion?: HubContentVersion;
    /**
     * The upper bound of the hub content schema version.
     */
    MaxSchemaVersion?: DocumentSchemaVersion;
    /**
     * Only list hub content versions that were created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Only list hub content versions that were created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Sort hub content versions by either name or creation time.
     */
    SortBy?: HubContentSortBy;
    /**
     * Sort hub content versions by ascending or descending order.
     */
    SortOrder?: SortOrder;
    /**
     * The maximum number of hub content versions to list.
     */
    MaxResults?: MaxResults;
    /**
     * If the response to a previous ListHubContentVersions request was truncated, the response includes a NextToken. To retrieve the next set of hub content versions, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListHubContentVersionsResponse {
    /**
     * The summaries of the listed hub content versions.
     */
    HubContentSummaries: HubContentInfoList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content versions, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListHubContentsRequest {
    /**
     * The name of the hub to list the contents of.
     */
    HubName: HubNameOrArn;
    /**
     * The type of hub content to list.
     */
    HubContentType: HubContentType;
    /**
     * Only list hub content if the name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * The upper bound of the hub content schema verion.
     */
    MaxSchemaVersion?: DocumentSchemaVersion;
    /**
     * Only list hub content that was created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Only list hub content that was created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Sort hub content versions by either name or creation time.
     */
    SortBy?: HubContentSortBy;
    /**
     * Sort hubs by ascending or descending order.
     */
    SortOrder?: SortOrder;
    /**
     * The maximum amount of hub content to list.
     */
    MaxResults?: MaxResults;
    /**
     * If the response to a previous ListHubContents request was truncated, the response includes a NextToken. To retrieve the next set of hub content, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListHubContentsResponse {
    /**
     * The summaries of the listed hub content.
     */
    HubContentSummaries: HubContentInfoList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of hub content, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListHubsRequest {
    /**
     * Only list hubs with names that contain the specified string.
     */
    NameContains?: NameContains;
    /**
     * Only list hubs that were created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Only list hubs that were created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Only list hubs that were last modified before the time specified.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Only list hubs that were last modified after the time specified.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Sort hubs by either name or creation time.
     */
    SortBy?: HubSortBy;
    /**
     * Sort hubs by ascending or descending order.
     */
    SortOrder?: SortOrder;
    /**
     * The maximum number of hubs to list.
     */
    MaxResults?: MaxResults;
    /**
     * If the response to a previous ListHubs request was truncated, the response includes a NextToken. To retrieve the next set of hubs, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListHubsResponse {
    /**
     * The summaries of the listed hubs.
     */
    HubSummaries: HubInfoList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of hubs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListHumanTaskUisRequest {
    /**
     * A filter that returns only human task user interfaces with a creation time greater than or equal to the specified timestamp.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only human task user interfaces that were created before the specified timestamp.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * An optional value that specifies whether you want the results sorted in Ascending or Descending order.
     */
    SortOrder?: SortOrder;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
    /**
     * The total number of items to return. If the total number of available items is more than the value specified in MaxResults, then a NextToken will be provided in the output that you can use to resume pagination.
     */
    MaxResults?: MaxResults;
  }
  export interface ListHumanTaskUisResponse {
    /**
     * An array of objects describing the human task user interfaces.
     */
    HumanTaskUiSummaries: HumanTaskUiSummaries;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
  }
  export interface ListHyperParameterTuningJobsRequest {
    /**
     * If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of tuning jobs to return. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * The field to sort results by. The default is Name.
     */
    SortBy?: HyperParameterTuningJobSortByOptions;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only tuning jobs that were created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only tuning jobs that were created before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only tuning jobs that were modified after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only tuning jobs that were modified before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A filter that returns only tuning jobs with the specified status.
     */
    StatusEquals?: HyperParameterTuningJobStatus;
  }
  export interface ListHyperParameterTuningJobsResponse {
    /**
     * A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.
     */
    HyperParameterTuningJobSummaries: HyperParameterTuningJobSummaries;
    /**
     * If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListImageVersionsRequest {
    /**
     * A filter that returns only versions created on or after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only versions created on or before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * The name of the image to list the versions of.
     */
    ImageName: ImageName;
    /**
     * A filter that returns only versions modified on or after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only versions modified on or before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * The maximum number of versions to return in the response. The default value is 10. 
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListImageVersions didn't return the full set of versions, the call returns a token for getting the next set of versions.
     */
    NextToken?: NextToken;
    /**
     * The property used to sort results. The default value is CREATION_TIME.
     */
    SortBy?: ImageVersionSortBy;
    /**
     * The sort order. The default value is DESCENDING.
     */
    SortOrder?: ImageVersionSortOrder;
  }
  export interface ListImageVersionsResponse {
    /**
     * A list of versions and their properties.
     */
    ImageVersions?: ImageVersions;
    /**
     * A token for getting the next set of versions, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListImagesRequest {
    /**
     * A filter that returns only images created on or after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only images created on or before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only images modified on or after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only images modified on or before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * The maximum number of images to return in the response. The default value is 10. 
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns only images whose name contains the specified string.
     */
    NameContains?: ImageNameContains;
    /**
     * If the previous call to ListImages didn't return the full set of images, the call returns a token for getting the next set of images.
     */
    NextToken?: NextToken;
    /**
     * The property used to sort results. The default value is CREATION_TIME.
     */
    SortBy?: ImageSortBy;
    /**
     * The sort order. The default value is DESCENDING.
     */
    SortOrder?: ImageSortOrder;
  }
  export interface ListImagesResponse {
    /**
     * A list of images and their properties.
     */
    Images?: Images;
    /**
     * A token for getting the next set of images, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListInferenceComponentsInput {
    /**
     * The field by which to sort the inference components in the response. The default is CreationTime.
     */
    SortBy?: InferenceComponentSortKey;
    /**
     * The sort order for results. The default is Descending.
     */
    SortOrder?: OrderKey;
    /**
     * A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
     */
    NextToken?: PaginationToken;
    /**
     * The maximum number of inference components to return in the response. This value defaults to 10.
     */
    MaxResults?: MaxResults;
    /**
     * Filters the results to only those inference components with a name that contains the specified string.
     */
    NameContains?: InferenceComponentNameContains;
    /**
     * Filters the results to only those inference components that were created before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Filters the results to only those inference components that were created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Filters the results to only those inference components that were updated before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * Filters the results to only those inference components that were updated after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Filters the results to only those inference components with the specified status.
     */
    StatusEquals?: InferenceComponentStatus;
    /**
     * An endpoint name to filter the listed inference components. The response includes only those inference components that are hosted at the specified endpoint.
     */
    EndpointNameEquals?: EndpointName;
    /**
     * A production variant name to filter the listed inference components. The response includes only those inference components that are hosted at the specified variant.
     */
    VariantNameEquals?: VariantName;
  }
  export interface ListInferenceComponentsOutput {
    /**
     * A list of inference components and their properties that matches any of the filters you specified in the request.
     */
    InferenceComponents: InferenceComponentSummaryList;
    /**
     * The token to use in a subsequent request to get the next set of results following a truncated response.
     */
    NextToken?: PaginationToken;
  }
  export interface ListInferenceExperimentsRequest {
    /**
     * Selects inference experiments whose names contain this name.
     */
    NameContains?: NameContains;
    /**
     *  Selects inference experiments of this type. For the possible types of inference experiments, see CreateInferenceExperiment. 
     */
    Type?: InferenceExperimentType;
    /**
     *  Selects inference experiments which are in this status. For the possible statuses, see DescribeInferenceExperiment. 
     */
    StatusEquals?: InferenceExperimentStatus;
    /**
     * Selects inference experiments which were created after this timestamp.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Selects inference experiments which were created before this timestamp.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Selects inference experiments which were last modified after this timestamp.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * Selects inference experiments which were last modified before this timestamp.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * The column by which to sort the listed inference experiments.
     */
    SortBy?: SortInferenceExperimentsBy;
    /**
     * The direction of sorting (ascending or descending).
     */
    SortOrder?: SortOrder;
    /**
     *  The response from the last list when returning a list large enough to need tokening. 
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to select.
     */
    MaxResults?: MaxResults;
  }
  export interface ListInferenceExperimentsResponse {
    /**
     * List of inference experiments.
     */
    InferenceExperiments?: InferenceExperimentList;
    /**
     * The token to use when calling the next page of results.
     */
    NextToken?: NextToken;
  }
  export interface ListInferenceRecommendationsJobStepsRequest {
    /**
     * The name for the Inference Recommender job.
     */
    JobName: RecommendationJobName;
    /**
     * A filter to return benchmarks of a specified status. If this field is left empty, then all benchmarks are returned.
     */
    Status?: RecommendationJobStatus;
    /**
     * A filter to return details about the specified type of subtask.  BENCHMARK: Evaluate the performance of your model on different instance types.
     */
    StepType?: RecommendationStepType;
    /**
     * The maximum number of results to return.
     */
    MaxResults?: MaxResults;
    /**
     * A token that you can specify to return more results from the list. Specify this field if you have a token that was returned from a previous request.
     */
    NextToken?: NextToken;
  }
  export interface ListInferenceRecommendationsJobStepsResponse {
    /**
     * A list of all subtask details in Inference Recommender.
     */
    Steps?: InferenceRecommendationsJobSteps;
    /**
     * A token that you can specify in your next request to return more results from the list.
     */
    NextToken?: NextToken;
  }
  export interface ListInferenceRecommendationsJobsRequest {
    /**
     * A filter that returns only jobs created after the specified time (timestamp).
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only jobs created before the specified time (timestamp).
     */
    CreationTimeBefore?: CreationTime;
    /**
     * A filter that returns only jobs that were last modified after the specified time (timestamp).
     */
    LastModifiedTimeAfter?: LastModifiedTime;
    /**
     * A filter that returns only jobs that were last modified before the specified time (timestamp).
     */
    LastModifiedTimeBefore?: LastModifiedTime;
    /**
     * A string in the job name. This filter returns only recommendations whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that retrieves only inference recommendations jobs with a specific status.
     */
    StatusEquals?: RecommendationJobStatus;
    /**
     * The parameter by which to sort the results.
     */
    SortBy?: ListInferenceRecommendationsJobsSortBy;
    /**
     * The sort order for the results.
     */
    SortOrder?: SortOrder;
    /**
     * If the response to a previous ListInferenceRecommendationsJobsRequest request was truncated, the response includes a NextToken. To retrieve the next set of recommendations, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of recommendations to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns only jobs that were created for this model.
     */
    ModelNameEquals?: ModelName;
    /**
     * A filter that returns only jobs that were created for this versioned model package.
     */
    ModelPackageVersionArnEquals?: ModelPackageArn;
  }
  export interface ListInferenceRecommendationsJobsResponse {
    /**
     * The recommendations created from the Amazon SageMaker Inference Recommender job.
     */
    InferenceRecommendationsJobs: InferenceRecommendationsJobs;
    /**
     * A token for getting the next set of recommendations, if there are any.
     */
    NextToken?: NextToken;
  }
  export type ListInferenceRecommendationsJobsSortBy = "Name"|"CreationTime"|"Status"|string;
  export interface ListLabelingJobsForWorkteamRequest {
    /**
     * The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.
     */
    WorkteamArn: WorkteamArn;
    /**
     * The maximum number of labeling jobs to return in each page of the response.
     */
    MaxResults?: MaxResults;
    /**
     * If the result of the previous ListLabelingJobsForWorkteam request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * A filter that returns only labeling jobs created after the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only labeling jobs created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter the limits jobs to only the ones whose job reference code contains the specified string.
     */
    JobReferenceCodeContains?: JobReferenceCodeContains;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: ListLabelingJobsForWorkteamSortByOptions;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListLabelingJobsForWorkteamResponse {
    /**
     * An array of LabelingJobSummary objects, each describing a labeling job.
     */
    LabelingJobSummaryList: LabelingJobForWorkteamSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export type ListLabelingJobsForWorkteamSortByOptions = "CreationTime"|string;
  export interface ListLabelingJobsRequest {
    /**
     * A filter that returns only labeling jobs created after the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only labeling jobs created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only labeling jobs modified after the specified time (timestamp).
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only labeling jobs modified before the specified time (timestamp).
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * The maximum number of labeling jobs to return in each page of the response.
     */
    MaxResults?: MaxResults;
    /**
     * If the result of the previous ListLabelingJobs request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * A string in the labeling job name. This filter returns only labeling jobs whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: SortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * A filter that retrieves only labeling jobs with a specific status.
     */
    StatusEquals?: LabelingJobStatus;
  }
  export interface ListLabelingJobsResponse {
    /**
     * An array of LabelingJobSummary objects, each describing a labeling job.
     */
    LabelingJobSummaryList?: LabelingJobSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of labeling jobs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export type ListLineageEntityParameterKey = StringParameterValue[];
  export interface ListLineageGroupsRequest {
    /**
     * A timestamp to filter against lineage groups created after a certain point in time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A timestamp to filter against lineage groups created before a certain point in time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: SortLineageGroupsBy;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of endpoints to return in the response. This value defaults to 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListLineageGroupsResponse {
    /**
     * A list of lineage groups and their properties.
     */
    LineageGroupSummaries?: LineageGroupSummaries;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of algorithms, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export type ListMaxResults = number;
  export interface ListMlflowTrackingServersRequest {
    /**
     * Use the CreatedAfter filter to only list tracking servers created after a specific date and time. Listed tracking servers are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedAfter parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
     */
    CreatedAfter?: Timestamp;
    /**
     * Use the CreatedBefore filter to only list tracking servers created before a specific date and time. Listed tracking servers are shown with a date and time such as "2024-03-16T01:46:56+00:00". The CreatedBefore parameter takes in a Unix timestamp. To convert a date and time into a Unix timestamp, see EpochConverter.
     */
    CreatedBefore?: Timestamp;
    /**
     * Filter for tracking servers with a specified creation status.
     */
    TrackingServerStatus?: TrackingServerStatus;
    /**
     * Filter for tracking servers using the specified MLflow version.
     */
    MlflowVersion?: MlflowVersion;
    /**
     * Filter for trackings servers sorting by name, creation time, or creation status.
     */
    SortBy?: SortTrackingServerBy;
    /**
     * Change the order of the listed tracking servers. By default, tracking servers are listed in Descending order by creation time. To change the list order, you can specify SortOrder to be Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of tracking servers to list.
     */
    MaxResults?: MaxResults;
  }
  export interface ListMlflowTrackingServersResponse {
    /**
     * A list of tracking servers according to chosen filters.
     */
    TrackingServerSummaries?: TrackingServerSummaryList;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListModelBiasJobDefinitionsRequest {
    /**
     * Name of the endpoint to monitor for model bias.
     */
    EndpointName?: EndpointName;
    /**
     * Whether to sort results by the Name or CreationTime field. The default is CreationTime.
     */
    SortBy?: MonitoringJobDefinitionSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of model bias jobs to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * Filter for model bias jobs whose name contains a specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only model bias jobs created before a specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only model bias jobs created after a specified time.
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListModelBiasJobDefinitionsResponse {
    /**
     * A JSON array in which each element is a summary for a model bias jobs.
     */
    JobDefinitionSummaries: MonitoringJobDefinitionSummaryList;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelCardExportJobsRequest {
    /**
     * List export jobs for the model card with the specified name.
     */
    ModelCardName: EntityName;
    /**
     * List export jobs for the model card with the specified version.
     */
    ModelCardVersion?: Integer;
    /**
     * Only list model card export jobs that were created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Only list model card export jobs that were created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * Only list model card export jobs with names that contain the specified string.
     */
    ModelCardExportJobNameContains?: EntityName;
    /**
     * Only list model card export jobs with the specified status.
     */
    StatusEquals?: ModelCardExportJobStatus;
    /**
     * Sort model card export jobs by either name or creation time. Sorts by creation time by default.
     */
    SortBy?: ModelCardExportJobSortBy;
    /**
     * Sort model card export jobs by ascending or descending order.
     */
    SortOrder?: ModelCardExportJobSortOrder;
    /**
     * If the response to a previous ListModelCardExportJobs request was truncated, the response includes a NextToken. To retrieve the next set of model card export jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of model card export jobs to list.
     */
    MaxResults?: MaxResults;
  }
  export interface ListModelCardExportJobsResponse {
    /**
     * The summaries of the listed model card export jobs.
     */
    ModelCardExportJobSummaries: ModelCardExportJobSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of model card export jobs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelCardVersionsRequest {
    /**
     * Only list model card versions that were created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Only list model card versions that were created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * The maximum number of model card versions to list.
     */
    MaxResults?: MaxResults;
    /**
     * List model card versions for the model card with the specified name or Amazon Resource Name (ARN).
     */
    ModelCardName: ModelCardNameOrArn;
    /**
     * Only list model card versions with the specified approval status.
     */
    ModelCardStatus?: ModelCardStatus;
    /**
     * If the response to a previous ListModelCardVersions request was truncated, the response includes a NextToken. To retrieve the next set of model card versions, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * Sort listed model card versions by version. Sorts by version by default.
     */
    SortBy?: ModelCardVersionSortBy;
    /**
     * Sort model card versions by ascending or descending order.
     */
    SortOrder?: ModelCardSortOrder;
  }
  export interface ListModelCardVersionsResponse {
    /**
     * The summaries of the listed versions of the model card.
     */
    ModelCardVersionSummaryList: ModelCardVersionSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of model card versions, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelCardsRequest {
    /**
     * Only list model cards that were created after the time specified.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * Only list model cards that were created before the time specified.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * The maximum number of model cards to list.
     */
    MaxResults?: MaxResults;
    /**
     * Only list model cards with names that contain the specified string.
     */
    NameContains?: EntityName;
    /**
     * Only list model cards with the specified approval status.
     */
    ModelCardStatus?: ModelCardStatus;
    /**
     * If the response to a previous ListModelCards request was truncated, the response includes a NextToken. To retrieve the next set of model cards, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * Sort model cards by either name or creation time. Sorts by creation time by default.
     */
    SortBy?: ModelCardSortBy;
    /**
     * Sort model cards by ascending or descending order.
     */
    SortOrder?: ModelCardSortOrder;
  }
  export interface ListModelCardsResponse {
    /**
     * The summaries of the listed model cards.
     */
    ModelCardSummaries: ModelCardSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of model cards, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelExplainabilityJobDefinitionsRequest {
    /**
     * Name of the endpoint to monitor for model explainability.
     */
    EndpointName?: EndpointName;
    /**
     * Whether to sort results by the Name or CreationTime field. The default is CreationTime.
     */
    SortBy?: MonitoringJobDefinitionSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of jobs to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * Filter for model explainability jobs whose name contains a specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only model explainability jobs created before a specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only model explainability jobs created after a specified time.
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListModelExplainabilityJobDefinitionsResponse {
    /**
     * A JSON array in which each element is a summary for a explainability bias jobs.
     */
    JobDefinitionSummaries: MonitoringJobDefinitionSummaryList;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelMetadataRequest {
    /**
     * One or more filters that searches for the specified resource or resources in a search. All resource objects that satisfy the expression's condition are included in the search results. Specify the Framework, FrameworkVersion, Domain or Task to filter supported. Filter names and values are case-sensitive.
     */
    SearchExpression?: ModelMetadataSearchExpression;
    /**
     * If the response to a previous ListModelMetadataResponse request was truncated, the response includes a NextToken. To retrieve the next set of model metadata, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of models to return in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListModelMetadataResponse {
    /**
     * A structure that holds model metadata.
     */
    ModelMetadataSummaries: ModelMetadataSummaries;
    /**
     * A token for getting the next set of recommendations, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListModelPackageGroupsInput {
    /**
     * A filter that returns only model groups created after the specified time.
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only model groups created before the specified time.
     */
    CreationTimeBefore?: CreationTime;
    /**
     * The maximum number of results to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the model group name. This filter returns only model groups whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * If the result of the previous ListModelPackageGroups request was truncated, the response includes a NextToken. To retrieve the next set of model groups, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: ModelPackageGroupSortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * A filter that returns either model groups shared with you or model groups in your own account. When the value is CrossAccount, the results show the resources made discoverable to you from other accounts. When the value is SameAccount or null, the results show resources from your account. The default is SameAccount.
     */
    CrossAccountFilterOption?: CrossAccountFilterOption;
  }
  export interface ListModelPackageGroupsOutput {
    /**
     * A list of summaries of the model groups in your Amazon Web Services account.
     */
    ModelPackageGroupSummaryList: ModelPackageGroupSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of model groups, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelPackagesInput {
    /**
     * A filter that returns only model packages created after the specified time (timestamp).
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only model packages created before the specified time (timestamp).
     */
    CreationTimeBefore?: CreationTime;
    /**
     * The maximum number of model packages to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the model package name. This filter returns only model packages whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only the model packages with the specified approval status.
     */
    ModelApprovalStatus?: ModelApprovalStatus;
    /**
     * A filter that returns only model versions that belong to the specified model group.
     */
    ModelPackageGroupName?: ArnOrName;
    /**
     * A filter that returns only the model packages of the specified type. This can be one of the following values.    UNVERSIONED - List only unversioined models. This is the default value if no ModelPackageType is specified.    VERSIONED - List only versioned models.    BOTH - List both versioned and unversioned models.  
     */
    ModelPackageType?: ModelPackageType;
    /**
     * If the response to a previous ListModelPackages request was truncated, the response includes a NextToken. To retrieve the next set of model packages, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: ModelPackageSortBy;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListModelPackagesOutput {
    /**
     * An array of ModelPackageSummary objects, each of which lists a model package.
     */
    ModelPackageSummaryList: ModelPackageSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of model packages, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelQualityJobDefinitionsRequest {
    /**
     * A filter that returns only model quality monitoring job definitions that are associated with the specified endpoint.
     */
    EndpointName?: EndpointName;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: MonitoringJobDefinitionSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListModelQualityJobDefinitions request was truncated, the response includes a NextToken. To retrieve the next set of model quality monitoring job definitions, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to return in a call to ListModelQualityJobDefinitions.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the transform job name. This filter returns only model quality monitoring job definitions whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only model quality monitoring job definitions created before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only model quality monitoring job definitions created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListModelQualityJobDefinitionsResponse {
    /**
     * A list of summaries of model quality monitoring job definitions.
     */
    JobDefinitionSummaries: MonitoringJobDefinitionSummaryList;
    /**
     * If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of model quality monitoring job definitions, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListModelsInput {
    /**
     * Sorts the list of results. The default is CreationTime.
     */
    SortBy?: ModelSortKey;
    /**
     * The sort order for results. The default is Descending.
     */
    SortOrder?: OrderKey;
    /**
     * If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.
     */
    NextToken?: PaginationToken;
    /**
     * The maximum number of models to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A string in the model name. This filter returns only models whose name contains the specified string.
     */
    NameContains?: ModelNameContains;
    /**
     * A filter that returns only models created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
  }
  export interface ListModelsOutput {
    /**
     * An array of ModelSummary objects, each of which lists a model.
     */
    Models: ModelSummaryList;
    /**
     *  If the response is truncated, SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request. 
     */
    NextToken?: PaginationToken;
  }
  export interface ListMonitoringAlertHistoryRequest {
    /**
     * The name of a monitoring schedule.
     */
    MonitoringScheduleName?: MonitoringScheduleName;
    /**
     * The name of a monitoring alert.
     */
    MonitoringAlertName?: MonitoringAlertName;
    /**
     * The field used to sort results. The default is CreationTime.
     */
    SortBy?: MonitoringAlertHistorySortKey;
    /**
     * The sort order, whether Ascending or Descending, of the alert history. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListMonitoringAlertHistory request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to display. The default is 100.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns only alerts created on or before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only alerts created on or after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that retrieves only alerts with a specific status.
     */
    StatusEquals?: MonitoringAlertStatus;
  }
  export interface ListMonitoringAlertHistoryResponse {
    /**
     * An alert history for a model monitoring schedule.
     */
    MonitoringAlertHistory?: MonitoringAlertHistoryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListMonitoringAlertsRequest {
    /**
     * The name of a monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * If the result of the previous ListMonitoringAlerts request was truncated, the response includes a NextToken. To retrieve the next set of alerts in the history, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to display. The default is 100.
     */
    MaxResults?: MaxResults;
  }
  export interface ListMonitoringAlertsResponse {
    /**
     * A JSON array where each element is a summary for a monitoring alert.
     */
    MonitoringAlertSummaries?: MonitoringAlertSummaryList;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of alerts, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListMonitoringExecutionsRequest {
    /**
     * Name of a specific schedule to fetch jobs for.
     */
    MonitoringScheduleName?: MonitoringScheduleName;
    /**
     * Name of a specific endpoint to fetch jobs for.
     */
    EndpointName?: EndpointName;
    /**
     * Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.
     */
    SortBy?: MonitoringExecutionSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of jobs to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * Filter for jobs scheduled before a specified time.
     */
    ScheduledTimeBefore?: Timestamp;
    /**
     * Filter for jobs scheduled after a specified time.
     */
    ScheduledTimeAfter?: Timestamp;
    /**
     * A filter that returns only jobs created before a specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only jobs created after a specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only jobs modified after a specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A filter that returns only jobs modified before a specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that retrieves only jobs with a specific status.
     */
    StatusEquals?: ExecutionStatus;
    /**
     * Gets a list of the monitoring job runs of the specified monitoring job definitions.
     */
    MonitoringJobDefinitionName?: MonitoringJobDefinitionName;
    /**
     * A filter that returns only the monitoring job runs of the specified monitoring type.
     */
    MonitoringTypeEquals?: MonitoringType;
  }
  export interface ListMonitoringExecutionsResponse {
    /**
     * A JSON array in which each element is a summary for a monitoring execution.
     */
    MonitoringExecutionSummaries: MonitoringExecutionSummaryList;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListMonitoringSchedulesRequest {
    /**
     * Name of a specific endpoint to fetch schedules for.
     */
    EndpointName?: EndpointName;
    /**
     * Whether to sort the results by the Status, CreationTime, or ScheduledTime field. The default is CreationTime.
     */
    SortBy?: MonitoringScheduleSortKey;
    /**
     * Whether to sort the results in Ascending or Descending order. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of jobs to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * Filter for monitoring schedules whose name contains a specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that returns only monitoring schedules created before a specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only monitoring schedules created after a specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only monitoring schedules modified before a specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A filter that returns only monitoring schedules modified after a specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only monitoring schedules modified before a specified time.
     */
    StatusEquals?: ScheduleStatus;
    /**
     * Gets a list of the monitoring schedules for the specified monitoring job definition.
     */
    MonitoringJobDefinitionName?: MonitoringJobDefinitionName;
    /**
     * A filter that returns only the monitoring schedules for the specified monitoring type.
     */
    MonitoringTypeEquals?: MonitoringType;
  }
  export interface ListMonitoringSchedulesResponse {
    /**
     * A JSON array in which each element is a summary for a monitoring schedule.
     */
    MonitoringScheduleSummaries: MonitoringScheduleSummaryList;
    /**
     * The token returned if the response is truncated. To retrieve the next set of job executions, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListNotebookInstanceLifecycleConfigsInput {
    /**
     * If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of lifecycle configurations to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * Sorts the list of results. The default is CreationTime.
     */
    SortBy?: NotebookInstanceLifecycleConfigSortKey;
    /**
     * The sort order for results.
     */
    SortOrder?: NotebookInstanceLifecycleConfigSortOrder;
    /**
     * A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.
     */
    NameContains?: NotebookInstanceLifecycleConfigNameContains;
    /**
     * A filter that returns only lifecycle configurations that were created before the specified time (timestamp).
     */
    CreationTimeBefore?: CreationTime;
    /**
     * A filter that returns only lifecycle configurations that were created after the specified time (timestamp).
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).
     */
    LastModifiedTimeBefore?: LastModifiedTime;
    /**
     * A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).
     */
    LastModifiedTimeAfter?: LastModifiedTime;
  }
  export interface ListNotebookInstanceLifecycleConfigsOutput {
    /**
     * If the response is truncated, SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request. 
     */
    NextToken?: NextToken;
    /**
     * An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigs?: NotebookInstanceLifecycleConfigSummaryList;
  }
  export interface ListNotebookInstancesInput {
    /**
     *  If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.   You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.  
     */
    NextToken?: NextToken;
    /**
     * The maximum number of notebook instances to return.
     */
    MaxResults?: MaxResults;
    /**
     * The field to sort results by. The default is Name.
     */
    SortBy?: NotebookInstanceSortKey;
    /**
     * The sort order for results. 
     */
    SortOrder?: NotebookInstanceSortOrder;
    /**
     * A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.
     */
    NameContains?: NotebookInstanceNameContains;
    /**
     * A filter that returns only notebook instances that were created before the specified time (timestamp). 
     */
    CreationTimeBefore?: CreationTime;
    /**
     * A filter that returns only notebook instances that were created after the specified time (timestamp).
     */
    CreationTimeAfter?: CreationTime;
    /**
     * A filter that returns only notebook instances that were modified before the specified time (timestamp).
     */
    LastModifiedTimeBefore?: LastModifiedTime;
    /**
     * A filter that returns only notebook instances that were modified after the specified time (timestamp).
     */
    LastModifiedTimeAfter?: LastModifiedTime;
    /**
     * A filter that returns only notebook instances with the specified status.
     */
    StatusEquals?: NotebookInstanceStatus;
    /**
     * A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.
     */
    NotebookInstanceLifecycleConfigNameContains?: NotebookInstanceLifecycleConfigName;
    /**
     * A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.
     */
    DefaultCodeRepositoryContains?: CodeRepositoryContains;
    /**
     * A filter that returns only notebook instances with associated with the specified git repository.
     */
    AdditionalCodeRepositoryEquals?: CodeRepositoryNameOrUrl;
  }
  export interface ListNotebookInstancesOutput {
    /**
     * If the response to the previous ListNotebookInstances request was truncated, SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * An array of NotebookInstanceSummary objects, one for each notebook instance.
     */
    NotebookInstances?: NotebookInstanceSummaryList;
  }
  export interface ListOptimizationJobsRequest {
    /**
     * A token that you use to get the next set of results following a truncated response. If the response to the previous request was truncated, that response provides the value for this token.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of optimization jobs to return in the response. The default is 50.
     */
    MaxResults?: MaxResults;
    /**
     * Filters the results to only those optimization jobs that were created after the specified time.
     */
    CreationTimeAfter?: CreationTime;
    /**
     * Filters the results to only those optimization jobs that were created before the specified time.
     */
    CreationTimeBefore?: CreationTime;
    /**
     * Filters the results to only those optimization jobs that were updated after the specified time.
     */
    LastModifiedTimeAfter?: LastModifiedTime;
    /**
     * Filters the results to only those optimization jobs that were updated before the specified time.
     */
    LastModifiedTimeBefore?: LastModifiedTime;
    /**
     * Filters the results to only those optimization jobs that apply the specified optimization techniques. You can specify either Quantization or Compilation.
     */
    OptimizationContains?: NameContains;
    /**
     * Filters the results to only those optimization jobs with a name that contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * Filters the results to only those optimization jobs with the specified status.
     */
    StatusEquals?: OptimizationJobStatus;
    /**
     * The field by which to sort the optimization jobs in the response. The default is CreationTime 
     */
    SortBy?: ListOptimizationJobsSortBy;
    /**
     * The sort order for results. The default is Ascending 
     */
    SortOrder?: SortOrder;
  }
  export interface ListOptimizationJobsResponse {
    /**
     * A list of optimization jobs and their properties that matches any of the filters you specified in the request.
     */
    OptimizationJobSummaries: OptimizationJobSummaries;
    /**
     * The token to use in a subsequent request to get the next set of results following a truncated response.
     */
    NextToken?: NextToken;
  }
  export type ListOptimizationJobsSortBy = "Name"|"CreationTime"|"Status"|string;
  export interface ListPipelineExecutionStepsRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
    /**
     * If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of pipeline execution steps to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * The field by which to sort results. The default is CreatedTime.
     */
    SortOrder?: SortOrder;
  }
  export interface ListPipelineExecutionStepsResponse {
    /**
     * A list of PipeLineExecutionStep objects. Each PipeLineExecutionStep consists of StepName, StartTime, EndTime, StepStatus, and Metadata. Metadata is an object with properties for each job that contains relevant information about the job created by the step.
     */
    PipelineExecutionSteps?: PipelineExecutionStepList;
    /**
     * If the result of the previous ListPipelineExecutionSteps request was truncated, the response includes a NextToken. To retrieve the next set of pipeline execution steps, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListPipelineExecutionsRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineName: PipelineNameOrArn;
    /**
     * A filter that returns the pipeline executions that were created after a specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns the pipeline executions that were created before a specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The field by which to sort results. The default is CreatedTime.
     */
    SortBy?: SortPipelineExecutionsBy;
    /**
     * The sort order for results.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of pipeline executions to return in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListPipelineExecutionsResponse {
    /**
     * Contains a sorted list of pipeline execution summary objects matching the specified filters. Each run summary includes the Amazon Resource Name (ARN) of the pipeline execution, the run date, and the status. This list can be empty. 
     */
    PipelineExecutionSummaries?: PipelineExecutionSummaryList;
    /**
     * If the result of the previous ListPipelineExecutions request was truncated, the response includes a NextToken. To retrieve the next set of pipeline executions, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListPipelineParametersForExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
    /**
     * If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of parameters to return in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListPipelineParametersForExecutionResponse {
    /**
     * Contains a list of pipeline parameters. This list can be empty. 
     */
    PipelineParameters?: ParameterList;
    /**
     * If the result of the previous ListPipelineParametersForExecution request was truncated, the response includes a NextToken. To retrieve the next set of parameters, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListPipelinesRequest {
    /**
     * The prefix of the pipeline name.
     */
    PipelineNamePrefix?: PipelineName;
    /**
     * A filter that returns the pipelines that were created after a specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns the pipelines that were created before a specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The field by which to sort results. The default is CreatedTime.
     */
    SortBy?: SortPipelinesBy;
    /**
     * The sort order for results.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of pipelines to return in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListPipelinesResponse {
    /**
     * Contains a sorted list of PipelineSummary objects matching the specified filters. Each PipelineSummary consists of PipelineArn, PipelineName, ExperimentName, PipelineDescription, CreationTime, LastModifiedTime, LastRunTime, and RoleArn. This list can be empty. 
     */
    PipelineSummaries?: PipelineSummaryList;
    /**
     * If the result of the previous ListPipelines request was truncated, the response includes a NextToken. To retrieve the next set of pipelines, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListProcessingJobsRequest {
    /**
     * A filter that returns only processing jobs created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only processing jobs created after the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only processing jobs modified after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only processing jobs modified before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A string in the processing job name. This filter returns only processing jobs whose name contains the specified string.
     */
    NameContains?: String;
    /**
     * A filter that retrieves only processing jobs with a specific status.
     */
    StatusEquals?: ProcessingJobStatus;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: SortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListProcessingJobs request was truncated, the response includes a NextToken. To retrieve the next set of processing jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of processing jobs to return in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListProcessingJobsResponse {
    /**
     * An array of ProcessingJobSummary objects, each listing a processing job.
     */
    ProcessingJobSummaries: ProcessingJobSummaries;
    /**
     * If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of processing jobs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListProjectsInput {
    /**
     * A filter that returns the projects that were created after a specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns the projects that were created before a specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * The maximum number of projects to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns the projects whose name contains a specified string.
     */
    NameContains?: ProjectEntityName;
    /**
     * If the result of the previous ListProjects request was truncated, the response includes a NextToken. To retrieve the next set of projects, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The field by which to sort results. The default is CreationTime.
     */
    SortBy?: ProjectSortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: ProjectSortOrder;
  }
  export interface ListProjectsOutput {
    /**
     * A list of summaries of projects.
     */
    ProjectSummaryList: ProjectSummaryList;
    /**
     * If the result of the previous ListCompilationJobs request was truncated, the response includes a NextToken. To retrieve the next set of model compilation jobs, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListResourceCatalogsRequest {
    /**
     *  A string that partially matches one or more ResourceCatalogs names. Filters ResourceCatalog by name. 
     */
    NameContains?: ResourceCatalogName;
    /**
     *  Use this parameter to search for ResourceCatalogs created after a specific date and time. 
     */
    CreationTimeAfter?: Timestamp;
    /**
     *  Use this parameter to search for ResourceCatalogs created before a specific date and time. 
     */
    CreationTimeBefore?: Timestamp;
    /**
     *  The order in which the resource catalogs are listed. 
     */
    SortOrder?: ResourceCatalogSortOrder;
    /**
     *  The value on which the resource catalog list is sorted. 
     */
    SortBy?: ResourceCatalogSortBy;
    /**
     *  The maximum number of results returned by ListResourceCatalogs. 
     */
    MaxResults?: MaxResults;
    /**
     *  A token to resume pagination of ListResourceCatalogs results. 
     */
    NextToken?: NextToken;
  }
  export interface ListResourceCatalogsResponse {
    /**
     *  A list of the requested ResourceCatalogs. 
     */
    ResourceCatalogs?: ResourceCatalogList;
    /**
     *  A token to resume pagination of ListResourceCatalogs results. 
     */
    NextToken?: NextToken;
  }
  export interface ListSpacesRequest {
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
     */
    MaxResults?: MaxResults;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: SpaceSortKey;
    /**
     * A parameter to search for the domain ID.
     */
    DomainIdEquals?: DomainId;
    /**
     * A parameter by which to filter the results.
     */
    SpaceNameContains?: SpaceName;
  }
  export interface ListSpacesResponse {
    /**
     * The list of spaces.
     */
    Spaces?: SpaceList;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListStageDevicesRequest {
    /**
     * The response from the last list when returning a list large enough to neeed tokening.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of requests to select.
     */
    MaxResults?: ListMaxResults;
    /**
     * The name of the edge deployment plan.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * Toggle for excluding devices deployed in other stages.
     */
    ExcludeDevicesDeployedInOtherStage?: Boolean;
    /**
     * The name of the stage in the deployment.
     */
    StageName: EntityName;
  }
  export interface ListStageDevicesResponse {
    /**
     * List of summaries of devices allocated to the stage.
     */
    DeviceDeploymentSummaries: DeviceDeploymentSummaries;
    /**
     * The token to use when calling the next page of results.
     */
    NextToken?: NextToken;
  }
  export interface ListStudioLifecycleConfigsRequest {
    /**
     * The total number of items to return in the response. If the total number of items available is more than the value specified, a NextToken is provided in the response. To resume pagination, provide the NextToken value in the as part of a subsequent call. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListStudioLifecycleConfigs didn't return the full set of Lifecycle Configurations, the call returns a token for getting the next set of Lifecycle Configurations.
     */
    NextToken?: NextToken;
    /**
     * A string in the Lifecycle Configuration name. This filter returns only Lifecycle Configurations whose name contains the specified string.
     */
    NameContains?: StudioLifecycleConfigName;
    /**
     * A parameter to search for the App Type to which the Lifecycle Configuration is attached.
     */
    AppTypeEquals?: StudioLifecycleConfigAppType;
    /**
     * A filter that returns only Lifecycle Configurations created on or before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only Lifecycle Configurations created on or after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only Lifecycle Configurations modified before the specified time.
     */
    ModifiedTimeBefore?: Timestamp;
    /**
     * A filter that returns only Lifecycle Configurations modified after the specified time.
     */
    ModifiedTimeAfter?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: StudioLifecycleConfigSortKey;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListStudioLifecycleConfigsResponse {
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * A list of Lifecycle Configurations and their properties.
     */
    StudioLifecycleConfigs?: StudioLifecycleConfigsList;
  }
  export interface ListSubscribedWorkteamsRequest {
    /**
     * A string in the work team name. This filter returns only work teams whose name contains the specified string.
     */
    NameContains?: WorkteamName;
    /**
     * If the result of the previous ListSubscribedWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of work teams to return in each page of the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListSubscribedWorkteamsResponse {
    /**
     * An array of Workteam objects, each describing a work team.
     */
    SubscribedWorkteams: SubscribedWorkteams;
    /**
     * If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListTagsInput {
    /**
     * The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.
     */
    ResourceArn: ResourceArn;
    /**
     *  If the response to the previous ListTags request is truncated, SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request. 
     */
    NextToken?: NextToken;
    /**
     * Maximum number of tags to return.
     */
    MaxResults?: ListTagsMaxResults;
  }
  export type ListTagsMaxResults = number;
  export interface ListTagsOutput {
    /**
     * An array of Tag objects, each with a tag key and a value.
     */
    Tags?: TagList;
    /**
     *  If response is truncated, SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens. 
     */
    NextToken?: NextToken;
  }
  export interface ListTrainingJobsForHyperParameterTuningJobRequest {
    /**
     * The name of the tuning job whose training jobs you want to list.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
    /**
     * If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of training jobs to return. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns only training jobs with the specified status.
     */
    StatusEquals?: TrainingJobStatus;
    /**
     * The field to sort results by. The default is Name. If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.
     */
    SortBy?: TrainingJobSortByOptions;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
  }
  export interface ListTrainingJobsForHyperParameterTuningJobResponse {
    /**
     * A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.
     */
    TrainingJobSummaries: HyperParameterTrainingJobSummaries;
    /**
     * If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export interface ListTrainingJobsRequest {
    /**
     * If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request. 
     */
    NextToken?: NextToken;
    /**
     * The maximum number of training jobs to return in the response.
     */
    MaxResults?: MaxResults;
    /**
     * A filter that returns only training jobs created after the specified time (timestamp).
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only training jobs created before the specified time (timestamp).
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only training jobs modified after the specified time (timestamp).
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only training jobs modified before the specified time (timestamp).
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A string in the training job name. This filter returns only training jobs whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that retrieves only training jobs with a specific status.
     */
    StatusEquals?: TrainingJobStatus;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: SortBy;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * A filter that retrieves only training jobs with a specific warm pool status.
     */
    WarmPoolStatusEquals?: WarmPoolResourceStatus;
  }
  export interface ListTrainingJobsResponse {
    /**
     * An array of TrainingJobSummary objects, each listing a training job.
     */
    TrainingJobSummaries: TrainingJobSummaries;
    /**
     * If the response is truncated, SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export interface ListTransformJobsRequest {
    /**
     * A filter that returns only transform jobs created after the specified time.
     */
    CreationTimeAfter?: Timestamp;
    /**
     * A filter that returns only transform jobs created before the specified time.
     */
    CreationTimeBefore?: Timestamp;
    /**
     * A filter that returns only transform jobs modified after the specified time.
     */
    LastModifiedTimeAfter?: Timestamp;
    /**
     * A filter that returns only transform jobs modified before the specified time.
     */
    LastModifiedTimeBefore?: Timestamp;
    /**
     * A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.
     */
    NameContains?: NameContains;
    /**
     * A filter that retrieves only transform jobs with a specific status.
     */
    StatusEquals?: TransformJobStatus;
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: SortBy;
    /**
     * The sort order for results. The default is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of transform jobs to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
  }
  export interface ListTransformJobsResponse {
    /**
     * An array of TransformJobSummary objects.
     */
    TransformJobSummaries: TransformJobSummaries;
    /**
     * If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.
     */
    NextToken?: NextToken;
  }
  export type ListTrialComponentKey256 = TrialComponentKey256[];
  export interface ListTrialComponentsRequest {
    /**
     * A filter that returns only components that are part of the specified experiment. If you specify ExperimentName, you can't filter by SourceArn or TrialName.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * A filter that returns only components that are part of the specified trial. If you specify TrialName, you can't filter by ExperimentName or SourceArn.
     */
    TrialName?: ExperimentEntityName;
    /**
     * A filter that returns only components that have the specified source Amazon Resource Name (ARN). If you specify SourceArn, you can't filter by ExperimentName or TrialName.
     */
    SourceArn?: String256;
    /**
     * A filter that returns only components created after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only components created before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortTrialComponentsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The maximum number of components to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListTrialComponents didn't return the full set of components, the call returns a token for getting the next set of components.
     */
    NextToken?: NextToken;
  }
  export interface ListTrialComponentsResponse {
    /**
     * A list of the summaries of your trial components.
     */
    TrialComponentSummaries?: TrialComponentSummaries;
    /**
     * A token for getting the next set of components, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListTrialsRequest {
    /**
     * A filter that returns only trials that are part of the specified experiment.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * A filter that returns only trials that are associated with the specified trial component.
     */
    TrialComponentName?: ExperimentEntityName;
    /**
     * A filter that returns only trials created after the specified time.
     */
    CreatedAfter?: Timestamp;
    /**
     * A filter that returns only trials created before the specified time.
     */
    CreatedBefore?: Timestamp;
    /**
     * The property used to sort results. The default value is CreationTime.
     */
    SortBy?: SortTrialsBy;
    /**
     * The sort order. The default value is Descending.
     */
    SortOrder?: SortOrder;
    /**
     * The maximum number of trials to return in the response. The default value is 10.
     */
    MaxResults?: MaxResults;
    /**
     * If the previous call to ListTrials didn't return the full set of trials, the call returns a token for getting the next set of trials.
     */
    NextToken?: NextToken;
  }
  export interface ListTrialsResponse {
    /**
     * A list of the summaries of your trials.
     */
    TrialSummaries?: TrialSummaries;
    /**
     * A token for getting the next set of trials, if there are any.
     */
    NextToken?: NextToken;
  }
  export interface ListUserProfilesRequest {
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
    /**
     * This parameter defines the maximum number of results that can be return in a single response. The MaxResults parameter is an upper bound, not a target. If there are more results available than the value specified, a NextToken is provided in the response. The NextToken indicates that the user should get the next set of results by providing this token as a part of a subsequent call. The default value for MaxResults is 10.
     */
    MaxResults?: MaxResults;
    /**
     * The sort order for the results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * The parameter by which to sort the results. The default is CreationTime.
     */
    SortBy?: UserProfileSortKey;
    /**
     * A parameter by which to filter the results.
     */
    DomainIdEquals?: DomainId;
    /**
     * A parameter by which to filter the results.
     */
    UserProfileNameContains?: UserProfileName;
  }
  export interface ListUserProfilesResponse {
    /**
     * The list of user profiles.
     */
    UserProfiles?: UserProfileList;
    /**
     * If the previous response was truncated, you will receive this token. Use it in your next request to receive the next set of results.
     */
    NextToken?: NextToken;
  }
  export interface ListWorkforcesRequest {
    /**
     * Sort workforces using the workforce name or creation date.
     */
    SortBy?: ListWorkforcesSortByOptions;
    /**
     * Sort workforces in ascending or descending order.
     */
    SortOrder?: SortOrder;
    /**
     * A filter you can use to search for workforces using part of the workforce name.
     */
    NameContains?: WorkforceName;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of workforces returned in the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListWorkforcesResponse {
    /**
     * A list containing information about your workforce.
     */
    Workforces: Workforces;
    /**
     * A token to resume pagination.
     */
    NextToken?: NextToken;
  }
  export type ListWorkforcesSortByOptions = "Name"|"CreateDate"|string;
  export interface ListWorkteamsRequest {
    /**
     * The field to sort results by. The default is CreationTime.
     */
    SortBy?: ListWorkteamsSortByOptions;
    /**
     * The sort order for results. The default is Ascending.
     */
    SortOrder?: SortOrder;
    /**
     * A string in the work team's name. This filter returns only work teams whose name contains the specified string.
     */
    NameContains?: WorkteamName;
    /**
     * If the result of the previous ListWorkteams request was truncated, the response includes a NextToken. To retrieve the next set of labeling jobs, use the token in the next request.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of work teams to return in each page of the response.
     */
    MaxResults?: MaxResults;
  }
  export interface ListWorkteamsResponse {
    /**
     * An array of Workteam objects, each describing a work team.
     */
    Workteams: Workteams;
    /**
     * If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of work teams, use it in the subsequent request.
     */
    NextToken?: NextToken;
  }
  export type ListWorkteamsSortByOptions = "Name"|"CreateDate"|string;
  export type Long = number;
  export type MLFramework = string;
  export type ManagedInstanceScalingMaxInstanceCount = number;
  export type ManagedInstanceScalingMinInstanceCount = number;
  export type ManagedInstanceScalingStatus = "ENABLED"|"DISABLED"|string;
  export type MaxAutoMLJobRuntimeInSeconds = number;
  export type MaxCandidates = number;
  export type MaxConcurrentInvocationsPerInstance = number;
  export type MaxConcurrentTaskCount = number;
  export type MaxConcurrentTransforms = number;
  export type MaxHumanLabeledObjectCount = number;
  export type MaxNumberOfTests = number;
  export type MaxNumberOfTrainingJobs = number;
  export type MaxNumberOfTrainingJobsNotImproving = number;
  export type MaxParallelExecutionSteps = number;
  export type MaxParallelOfTests = number;
  export type MaxParallelTrainingJobs = number;
  export type MaxPayloadInMB = number;
  export type MaxPendingTimeInSeconds = number;
  export type MaxPercentageOfInputDatasetLabeled = number;
  export type MaxResults = number;
  export type MaxRuntimeInSeconds = number;
  export type MaxRuntimePerTrainingJobInSeconds = number;
  export type MaxWaitTimeInSeconds = number;
  export type MaximumExecutionTimeoutInSeconds = number;
  export type MaximumRetryAttempts = number;
  export type MediaType = string;
  export interface MemberDefinition {
    /**
     * The Amazon Cognito user group that is part of the work team.
     */
    CognitoMemberDefinition?: CognitoMemberDefinition;
    /**
     * A list user groups that exist in your OIDC Identity Provider (IdP). One to ten groups can be used to create a single private work team. When you add a user group to the list of Groups, you can add that user group to one or more private work teams. If you add a user group to a private work team, all workers in that user group are added to the work team.
     */
    OidcMemberDefinition?: OidcMemberDefinition;
  }
  export type MemberDefinitions = MemberDefinition[];
  export type MemoryInMb = number;
  export interface MetadataProperties {
    /**
     * The commit ID.
     */
    CommitId?: MetadataPropertyValue;
    /**
     * The repository.
     */
    Repository?: MetadataPropertyValue;
    /**
     * The entity this entity was generated by.
     */
    GeneratedBy?: MetadataPropertyValue;
    /**
     * The project ID.
     */
    ProjectId?: MetadataPropertyValue;
  }
  export type MetadataPropertyValue = string;
  export interface MetricData {
    /**
     * The name of the metric.
     */
    MetricName?: MetricName;
    /**
     * The value of the metric.
     */
    Value?: Float;
    /**
     * The date and time that the algorithm emitted the metric.
     */
    Timestamp?: Timestamp;
  }
  export type MetricDataList = MetricDatum[];
  export interface MetricDatum {
    /**
     * The name of the metric.
     */
    MetricName?: AutoMLMetricEnum;
    /**
     * The value of the metric.
     */
    Value?: Float;
    /**
     * The dataset split from which the AutoML job produced the metric.
     */
    Set?: MetricSetSource;
    /**
     * The name of the standard metric.   For definitions of the standard metrics, see  Autopilot candidate metrics . 
     */
    StandardMetricName?: AutoMLMetricExtendedEnum;
  }
  export interface MetricDefinition {
    /**
     * The name of the metric.
     */
    Name: MetricName;
    /**
     * A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see Defining metrics and environment variables.
     */
    Regex: MetricRegex;
  }
  export type MetricDefinitionList = MetricDefinition[];
  export type MetricName = string;
  export type MetricRegex = string;
  export type MetricSetSource = "Train"|"Validation"|"Test"|string;
  export interface MetricSpecification {
    /**
     * Information about a predefined metric.
     */
    Predefined?: PredefinedMetricSpecification;
    /**
     * Information about a customized metric.
     */
    Customized?: CustomizedMetricSpecification;
  }
  export type MetricValue = number;
  export interface MetricsSource {
    /**
     * The metric source content type.
     */
    ContentType: ContentType;
    /**
     * The hash key used for the metrics source.
     */
    ContentDigest?: ContentDigest;
    /**
     * The S3 URI for the metrics source.
     */
    S3Uri: S3Uri;
  }
  export type MinimumInstanceMetadataServiceVersion = string;
  export type MlTools = "DataWrangler"|"FeatureStore"|"EmrClusters"|"AutoMl"|"Experiments"|"Training"|"ModelEvaluation"|"Pipelines"|"Models"|"JumpStart"|"InferenceRecommender"|"Endpoints"|"Projects"|"InferenceOptimization"|string;
  export type MlflowVersion = string;
  export interface Model {
    /**
     * The name of the model.
     */
    ModelName?: ModelName;
    PrimaryContainer?: ContainerDefinition;
    /**
     * The containers in the inference pipeline.
     */
    Containers?: ContainerDefinitionList;
    InferenceExecutionConfig?: InferenceExecutionConfig;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that you specified for the model.
     */
    ExecutionRoleArn?: RoleArn;
    VpcConfig?: VpcConfig;
    /**
     * A timestamp that indicates when the model was created.
     */
    CreationTime?: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of the model.
     */
    ModelArn?: ModelArn;
    /**
     * Isolates the model container. No inbound or outbound network calls can be made to or from the model container.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * A list of key-value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
    /**
     * A set of recommended deployment configurations for the model.
     */
    DeploymentRecommendation?: DeploymentRecommendation;
  }
  export interface ModelAccessConfig {
    /**
     * Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
     */
    AcceptEula: AcceptEula;
  }
  export type ModelApprovalStatus = "Approved"|"Rejected"|"PendingManualApproval"|string;
  export type ModelArn = string;
  export interface ModelArtifacts {
    /**
     * The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.
     */
    S3ModelArtifacts: S3Uri;
  }
  export interface ModelBiasAppSpecification {
    /**
     * The container image to be run by the model bias job.
     */
    ImageUri: ImageUri;
    /**
     * JSON formatted S3 file that defines bias parameters. For more information on this JSON configuration file, see Configure bias parameters.
     */
    ConfigUri: S3Uri;
    /**
     * Sets the environment variables in the Docker container.
     */
    Environment?: MonitoringEnvironmentMap;
  }
  export interface ModelBiasBaselineConfig {
    /**
     * The name of the baseline model bias job.
     */
    BaseliningJobName?: ProcessingJobName;
    ConstraintsResource?: MonitoringConstraintsResource;
  }
  export interface ModelBiasJobInput {
    EndpointInput?: EndpointInput;
    /**
     * Input object for the batch transform job.
     */
    BatchTransformInput?: BatchTransformInput;
    /**
     * Location of ground truth labels to use in model bias job.
     */
    GroundTruthS3Input: MonitoringGroundTruthS3Input;
  }
  export type ModelCacheSetting = "Enabled"|"Disabled"|string;
  export interface ModelCard {
    /**
     * The Amazon Resource Name (ARN) of the model card.
     */
    ModelCardArn?: ModelCardArn;
    /**
     * The unique name of the model card.
     */
    ModelCardName?: EntityName;
    /**
     * The version of the model card.
     */
    ModelCardVersion?: Integer;
    /**
     * The content of the model card. Content uses the model card JSON schema and provided as a string.
     */
    Content?: ModelCardContent;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus?: ModelCardStatus;
    /**
     * The security configuration used to protect model card data.
     */
    SecurityConfig?: ModelCardSecurityConfig;
    /**
     * The date and time that the model card was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * The date and time that the model card was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * Key-value pairs used to manage metadata for the model card.
     */
    Tags?: TagList;
    /**
     * The unique name (ID) of the model.
     */
    ModelId?: String;
    /**
     * The risk rating of the model. Different organizations might have different criteria for model card risk ratings. For more information, see Risk ratings.
     */
    RiskRating?: String;
    /**
     * The model package group that contains the model package. Only relevant for model cards created for model packages in the Amazon SageMaker Model Registry. 
     */
    ModelPackageGroupName?: String;
  }
  export type ModelCardArn = string;
  export type ModelCardContent = string;
  export interface ModelCardExportArtifacts {
    /**
     * The Amazon S3 URI of the exported model artifacts.
     */
    S3ExportArtifacts: S3Uri;
  }
  export type ModelCardExportJobArn = string;
  export type ModelCardExportJobSortBy = "Name"|"CreationTime"|"Status"|string;
  export type ModelCardExportJobSortOrder = "Ascending"|"Descending"|string;
  export type ModelCardExportJobStatus = "InProgress"|"Completed"|"Failed"|string;
  export interface ModelCardExportJobSummary {
    /**
     * The name of the model card export job.
     */
    ModelCardExportJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model card export job.
     */
    ModelCardExportJobArn: ModelCardExportJobArn;
    /**
     * The completion status of the model card export job.
     */
    Status: ModelCardExportJobStatus;
    /**
     * The name of the model card that the export job exports.
     */
    ModelCardName: EntityName;
    /**
     * The version of the model card that the export job exports.
     */
    ModelCardVersion: Integer;
    /**
     * The date and time that the model card export job was created.
     */
    CreatedAt: Timestamp;
    /**
     * The date and time that the model card export job was last modified..
     */
    LastModifiedAt: Timestamp;
  }
  export type ModelCardExportJobSummaryList = ModelCardExportJobSummary[];
  export interface ModelCardExportOutputConfig {
    /**
     * The Amazon S3 output path to export your model card PDF.
     */
    S3OutputPath: S3Uri;
  }
  export type ModelCardNameOrArn = string;
  export type ModelCardProcessingStatus = "DeleteInProgress"|"DeletePending"|"ContentDeleted"|"ExportJobsDeleted"|"DeleteCompleted"|"DeleteFailed"|string;
  export interface ModelCardSecurityConfig {
    /**
     * A Key Management Service key ID to use for encrypting a model card.
     */
    KmsKeyId?: KmsKeyId;
  }
  export type ModelCardSortBy = "Name"|"CreationTime"|string;
  export type ModelCardSortOrder = "Ascending"|"Descending"|string;
  export type ModelCardStatus = "Draft"|"PendingReview"|"Approved"|"Archived"|string;
  export interface ModelCardSummary {
    /**
     * The name of the model card.
     */
    ModelCardName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model card.
     */
    ModelCardArn: ModelCardArn;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus: ModelCardStatus;
    /**
     * The date and time that the model card was created.
     */
    CreationTime: Timestamp;
    /**
     * The date and time that the model card was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type ModelCardSummaryList = ModelCardSummary[];
  export type ModelCardVersionSortBy = "Version"|string;
  export interface ModelCardVersionSummary {
    /**
     * The name of the model card.
     */
    ModelCardName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model card.
     */
    ModelCardArn: ModelCardArn;
    /**
     * The approval status of the model card version within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus: ModelCardStatus;
    /**
     * A version of the model card.
     */
    ModelCardVersion: Integer;
    /**
     * The date and time that the model card version was created.
     */
    CreationTime: Timestamp;
    /**
     * The time date and time that the model card version was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type ModelCardVersionSummaryList = ModelCardVersionSummary[];
  export interface ModelClientConfig {
    /**
     * The timeout value in seconds for an invocation request. The default value is 600.
     */
    InvocationsTimeoutInSeconds?: InvocationsTimeoutInSeconds;
    /**
     * The maximum number of retries when invocation requests are failing. The default value is 3.
     */
    InvocationsMaxRetries?: InvocationsMaxRetries;
  }
  export interface ModelCompilationConfig {
    /**
     * The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
     */
    Image?: OptimizationContainerImage;
    /**
     * Environment variables that override the default ones in the model container.
     */
    OverrideEnvironment?: OptimizationJobEnvironmentVariables;
  }
  export type ModelCompressionType = "None"|"Gzip"|string;
  export interface ModelConfiguration {
    /**
     * The inference specification name in the model package version.
     */
    InferenceSpecificationName?: InferenceSpecificationName;
    /**
     * Defines the environment parameters that includes key, value types, and values.
     */
    EnvironmentParameters?: EnvironmentParameters;
    /**
     * The name of the compilation job used to create the recommended model artifacts.
     */
    CompilationJobName?: RecommendationJobCompilationJobName;
  }
  export interface ModelDashboardEndpoint {
    /**
     * The endpoint name.
     */
    EndpointName: EndpointName;
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
    /**
     * A timestamp that indicates when the endpoint was created.
     */
    CreationTime: Timestamp;
    /**
     * The last time the endpoint was modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The endpoint status.
     */
    EndpointStatus: EndpointStatus;
  }
  export type ModelDashboardEndpoints = ModelDashboardEndpoint[];
  export interface ModelDashboardIndicatorAction {
    /**
     * Indicates whether the alert action is turned on.
     */
    Enabled?: Boolean;
  }
  export interface ModelDashboardModel {
    /**
     * A model displayed in the Model Dashboard.
     */
    Model?: Model;
    /**
     * The endpoints that host a model.
     */
    Endpoints?: ModelDashboardEndpoints;
    LastBatchTransformJob?: TransformJob;
    /**
     * The monitoring schedules for a model.
     */
    MonitoringSchedules?: ModelDashboardMonitoringSchedules;
    /**
     * The model card for a model.
     */
    ModelCard?: ModelDashboardModelCard;
  }
  export interface ModelDashboardModelCard {
    /**
     * The Amazon Resource Name (ARN) for a model card.
     */
    ModelCardArn?: ModelCardArn;
    /**
     * The name of a model card.
     */
    ModelCardName?: EntityName;
    /**
     * The model card version.
     */
    ModelCardVersion?: Integer;
    /**
     * The model card status.
     */
    ModelCardStatus?: ModelCardStatus;
    /**
     * The KMS Key ID (KMSKeyId) for encryption of model card information.
     */
    SecurityConfig?: ModelCardSecurityConfig;
    /**
     * A timestamp that indicates when the model card was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
    /**
     * A timestamp that indicates when the model card was last updated.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * The tags associated with a model card.
     */
    Tags?: TagList;
    /**
     * For models created in SageMaker, this is the model ARN. For models created outside of SageMaker, this is a user-customized string.
     */
    ModelId?: String;
    /**
     * A model card's risk rating. Can be low, medium, or high.
     */
    RiskRating?: String;
  }
  export interface ModelDashboardMonitoringSchedule {
    /**
     * The Amazon Resource Name (ARN) of a monitoring schedule.
     */
    MonitoringScheduleArn?: MonitoringScheduleArn;
    /**
     * The name of a monitoring schedule.
     */
    MonitoringScheduleName?: MonitoringScheduleName;
    /**
     * The status of the monitoring schedule.
     */
    MonitoringScheduleStatus?: ScheduleStatus;
    /**
     * The monitor type of a model monitor.
     */
    MonitoringType?: MonitoringType;
    /**
     * If a monitoring job failed, provides the reason.
     */
    FailureReason?: FailureReason;
    /**
     * A timestamp that indicates when the monitoring schedule was created.
     */
    CreationTime?: Timestamp;
    /**
     * A timestamp that indicates when the monitoring schedule was last updated.
     */
    LastModifiedTime?: Timestamp;
    MonitoringScheduleConfig?: MonitoringScheduleConfig;
    /**
     * The endpoint which is monitored.
     */
    EndpointName?: EndpointName;
    /**
     * A JSON array where each element is a summary for a monitoring alert.
     */
    MonitoringAlertSummaries?: MonitoringAlertSummaryList;
    LastMonitoringExecutionSummary?: MonitoringExecutionSummary;
    BatchTransformInput?: BatchTransformInput;
  }
  export type ModelDashboardMonitoringSchedules = ModelDashboardMonitoringSchedule[];
  export interface ModelDataQuality {
    /**
     * Data quality statistics for a model.
     */
    Statistics?: MetricsSource;
    /**
     * Data quality constraints for a model.
     */
    Constraints?: MetricsSource;
  }
  export interface ModelDataSource {
    /**
     * Specifies the S3 location of ML model data to deploy.
     */
    S3DataSource?: S3ModelDataSource;
  }
  export interface ModelDeployConfig {
    /**
     * Set to True to automatically generate an endpoint name for a one-click Autopilot model deployment; set to False otherwise. The default value is False.  If you set AutoGenerateEndpointName to True, do not specify the EndpointName; otherwise a 400 error is thrown. 
     */
    AutoGenerateEndpointName?: AutoGenerateEndpointName;
    /**
     * Specifies the endpoint name to use for a one-click Autopilot model deployment if the endpoint name is not generated automatically.  Specify the EndpointName if and only if you set AutoGenerateEndpointName to False; otherwise a 400 error is thrown. 
     */
    EndpointName?: EndpointName;
  }
  export interface ModelDeployResult {
    /**
     * The name of the endpoint to which the model has been deployed.  If model deployment fails, this field is omitted from the response. 
     */
    EndpointName?: EndpointName;
  }
  export interface ModelDigests {
    /**
     * Provides a hash value that uniquely identifies the stored model artifacts.
     */
    ArtifactDigest?: ArtifactDigest;
  }
  export interface ModelExplainabilityAppSpecification {
    /**
     * The container image to be run by the model explainability job.
     */
    ImageUri: ImageUri;
    /**
     * JSON formatted Amazon S3 file that defines explainability parameters. For more information on this JSON configuration file, see Configure model explainability parameters.
     */
    ConfigUri: S3Uri;
    /**
     * Sets the environment variables in the Docker container.
     */
    Environment?: MonitoringEnvironmentMap;
  }
  export interface ModelExplainabilityBaselineConfig {
    /**
     * The name of the baseline model explainability job.
     */
    BaseliningJobName?: ProcessingJobName;
    ConstraintsResource?: MonitoringConstraintsResource;
  }
  export interface ModelExplainabilityJobInput {
    EndpointInput?: EndpointInput;
    /**
     * Input object for the batch transform job.
     */
    BatchTransformInput?: BatchTransformInput;
  }
  export interface ModelInfrastructureConfig {
    /**
     * The inference option to which to deploy your model. Possible values are the following:    RealTime: Deploy to real-time inference.  
     */
    InfrastructureType: ModelInfrastructureType;
    /**
     * The infrastructure configuration for deploying the model to real-time inference.
     */
    RealTimeInferenceConfig: RealTimeInferenceConfig;
  }
  export type ModelInfrastructureType = "RealTimeInference"|string;
  export interface ModelInput {
    /**
     * The input configuration object for the model.
     */
    DataInputConfig: DataInputConfig;
  }
  export type ModelInsightsLocation = string;
  export interface ModelLatencyThreshold {
    /**
     * The model latency percentile threshold. Acceptable values are P95 and P99. For custom load tests, specify the value as P95.
     */
    Percentile?: String64;
    /**
     * The model latency percentile value in milliseconds.
     */
    ValueInMilliseconds?: Integer;
  }
  export type ModelLatencyThresholds = ModelLatencyThreshold[];
  export interface ModelMetadataFilter {
    /**
     * The name of the of the model to filter by.
     */
    Name: ModelMetadataFilterType;
    /**
     * The value to filter the model metadata.
     */
    Value: String256;
  }
  export type ModelMetadataFilterType = "Domain"|"Framework"|"Task"|"FrameworkVersion"|string;
  export type ModelMetadataFilters = ModelMetadataFilter[];
  export interface ModelMetadataSearchExpression {
    /**
     * A list of filter objects.
     */
    Filters?: ModelMetadataFilters;
  }
  export type ModelMetadataSummaries = ModelMetadataSummary[];
  export interface ModelMetadataSummary {
    /**
     * The machine learning domain of the model.
     */
    Domain: String;
    /**
     * The machine learning framework of the model.
     */
    Framework: String;
    /**
     * The machine learning task of the model.
     */
    Task: String;
    /**
     * The name of the model.
     */
    Model: String;
    /**
     * The framework version of the model.
     */
    FrameworkVersion: String;
  }
  export interface ModelMetrics {
    /**
     * Metrics that measure the quality of a model.
     */
    ModelQuality?: ModelQuality;
    /**
     * Metrics that measure the quality of the input data for a model.
     */
    ModelDataQuality?: ModelDataQuality;
    /**
     * Metrics that measure bias in a model.
     */
    Bias?: Bias;
    /**
     * Metrics that help explain a model.
     */
    Explainability?: Explainability;
  }
  export type ModelName = string;
  export type ModelNameContains = string;
  export interface ModelPackage {
    /**
     * The name of the model.
     */
    ModelPackageName?: EntityName;
    /**
     * The model group to which the model belongs.
     */
    ModelPackageGroupName?: EntityName;
    /**
     * The version number of a versioned model.
     */
    ModelPackageVersion?: ModelPackageVersion;
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    ModelPackageArn?: ModelPackageArn;
    /**
     * The description of the model package.
     */
    ModelPackageDescription?: EntityDescription;
    /**
     * The time that the model package was created.
     */
    CreationTime?: CreationTime;
    /**
     * Defines how to perform inference generation after a training job is run.
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * A list of algorithms that were used to create a model package.
     */
    SourceAlgorithmSpecification?: SourceAlgorithmSpecification;
    /**
     * Specifies batch transform jobs that SageMaker runs to validate your model package.
     */
    ValidationSpecification?: ModelPackageValidationSpecification;
    /**
     * The status of the model package. This can be one of the following values.    PENDING - The model package is pending being created.    IN_PROGRESS - The model package is in the process of being created.    COMPLETED - The model package was successfully created.    FAILED - The model package failed.    DELETING - The model package is in the process of being deleted.  
     */
    ModelPackageStatus?: ModelPackageStatus;
    /**
     * Specifies the validation and image scan statuses of the model package.
     */
    ModelPackageStatusDetails?: ModelPackageStatusDetails;
    /**
     * Whether the model package is to be certified to be listed on Amazon Web Services Marketplace. For information about listing model packages on Amazon Web Services Marketplace, see List Your Algorithm or Model Package on Amazon Web Services Marketplace.
     */
    CertifyForMarketplace?: CertifyForMarketplace;
    /**
     * The approval status of the model. This can be one of the following values.    APPROVED - The model is approved    REJECTED - The model is rejected.    PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.  
     */
    ModelApprovalStatus?: ModelApprovalStatus;
    /**
     * Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
     */
    CreatedBy?: UserContext;
    /**
     * Metadata properties of the tracking entity, trial, or trial component.
     */
    MetadataProperties?: MetadataProperties;
    /**
     * Metrics for the model.
     */
    ModelMetrics?: ModelMetrics;
    /**
     * The last time the model package was modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * Information about the user who created or modified an experiment, trial, trial component, lineage group, or project.
     */
    LastModifiedBy?: UserContext;
    /**
     * A description provided when the model approval is set.
     */
    ApprovalDescription?: ApprovalDescription;
    /**
     * The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.
     */
    Domain?: String;
    /**
     * The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification.
     */
    Task?: String;
    /**
     * The Amazon Simple Storage Service path where the sample payload are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
     */
    SamplePayloadUrl?: String;
    /**
     * An array of additional Inference Specification objects.
     */
    AdditionalInferenceSpecifications?: AdditionalInferenceSpecifications;
    /**
     * The URI of the source for the model package.
     */
    SourceUri?: ModelPackageSourceUri;
    SecurityConfig?: ModelPackageSecurityConfig;
    ModelCard?: ModelPackageModelCard;
    /**
     * A list of the tags associated with the model package. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
    /**
     * The metadata properties for the model package. 
     */
    CustomerMetadataProperties?: CustomerMetadataMap;
    /**
     * Represents the drift check baselines that can be used when the model monitor is set using the model package.
     */
    DriftCheckBaselines?: DriftCheckBaselines;
    /**
     * Indicates if you want to skip model validation.
     */
    SkipModelValidation?: SkipModelValidation;
  }
  export type ModelPackageArn = string;
  export type ModelPackageArnList = ModelPackageArn[];
  export interface ModelPackageContainerDefinition {
    /**
     * The DNS host name for the Docker container.
     */
    ContainerHostname?: ContainerHostname;
    /**
     * The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. If you are using your own custom algorithm instead of an algorithm provided by SageMaker, the inference code must meet SageMaker requirements. SageMaker supports both registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker.
     */
    Image: ContainerImage;
    /**
     * An MD5 hash of the training algorithm that identifies the Docker image used for training.
     */
    ImageDigest?: ImageDigest;
    /**
     * The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).  The model artifacts must be in an S3 bucket that is in the same region as the model package. 
     */
    ModelDataUrl?: Url;
    /**
     * Specifies the location of ML model data to deploy during endpoint creation.
     */
    ModelDataSource?: ModelDataSource;
    /**
     * The Amazon Web Services Marketplace product ID of the model package.
     */
    ProductId?: ProductId;
    /**
     * The environment variables to set in the Docker container. Each key and value in the Environment string to string map can have length of up to 1024. We support up to 16 entries in the map.
     */
    Environment?: EnvironmentMap;
    /**
     * A structure with Model Input details.
     */
    ModelInput?: ModelInput;
    /**
     * The machine learning framework of the model package container image.
     */
    Framework?: String;
    /**
     * The framework version of the Model Package Container Image.
     */
    FrameworkVersion?: ModelPackageFrameworkVersion;
    /**
     * The name of a pre-trained machine learning benchmarked by Amazon SageMaker Inference Recommender model that matches your model. You can find a list of benchmarked models by calling ListModelMetadata.
     */
    NearestModelName?: String;
    /**
     * The additional data source that is used during inference in the Docker container for your model package.
     */
    AdditionalS3DataSource?: AdditionalS3DataSource;
  }
  export type ModelPackageContainerDefinitionList = ModelPackageContainerDefinition[];
  export type ModelPackageFrameworkVersion = string;
  export interface ModelPackageGroup {
    /**
     * The name of the model group.
     */
    ModelPackageGroupName?: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model group.
     */
    ModelPackageGroupArn?: ModelPackageGroupArn;
    /**
     * The description for the model group.
     */
    ModelPackageGroupDescription?: EntityDescription;
    /**
     * The time that the model group was created.
     */
    CreationTime?: CreationTime;
    CreatedBy?: UserContext;
    /**
     * The status of the model group. This can be one of the following values.    PENDING - The model group is pending being created.    IN_PROGRESS - The model group is in the process of being created.    COMPLETED - The model group was successfully created.    FAILED - The model group failed.    DELETING - The model group is in the process of being deleted.    DELETE_FAILED - SageMaker failed to delete the model group.  
     */
    ModelPackageGroupStatus?: ModelPackageGroupStatus;
    /**
     * A list of the tags associated with the model group. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
  }
  export type ModelPackageGroupArn = string;
  export type ModelPackageGroupSortBy = "Name"|"CreationTime"|string;
  export type ModelPackageGroupStatus = "Pending"|"InProgress"|"Completed"|"Failed"|"Deleting"|"DeleteFailed"|string;
  export interface ModelPackageGroupSummary {
    /**
     * The name of the model group.
     */
    ModelPackageGroupName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the model group.
     */
    ModelPackageGroupArn: ModelPackageGroupArn;
    /**
     * A description of the model group.
     */
    ModelPackageGroupDescription?: EntityDescription;
    /**
     * The time that the model group was created.
     */
    CreationTime: CreationTime;
    /**
     * The status of the model group.
     */
    ModelPackageGroupStatus: ModelPackageGroupStatus;
  }
  export type ModelPackageGroupSummaryList = ModelPackageGroupSummary[];
  export interface ModelPackageModelCard {
    /**
     * The content of the model card. The content must follow the schema described in Model Package Model Card Schema.
     */
    ModelCardContent?: ModelCardContent;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates can be made to the model card content. If you try to update the model card content, you will receive the message Model Card is in Archived state.  
     */
    ModelCardStatus?: ModelCardStatus;
  }
  export interface ModelPackageSecurityConfig {
    /**
     * The KMS Key ID (KMSKeyId) used for encryption of model package information.
     */
    KmsKeyId: KmsKeyId;
  }
  export type ModelPackageSortBy = "Name"|"CreationTime"|string;
  export type ModelPackageSourceUri = string;
  export type ModelPackageStatus = "Pending"|"InProgress"|"Completed"|"Failed"|"Deleting"|string;
  export interface ModelPackageStatusDetails {
    /**
     * The validation status of the model package.
     */
    ValidationStatuses: ModelPackageStatusItemList;
    /**
     * The status of the scan of the Docker image container for the model package.
     */
    ImageScanStatuses?: ModelPackageStatusItemList;
  }
  export interface ModelPackageStatusItem {
    /**
     * The name of the model package for which the overall status is being reported.
     */
    Name: EntityName;
    /**
     * The current status.
     */
    Status: DetailedModelPackageStatus;
    /**
     * if the overall status is Failed, the reason for the failure.
     */
    FailureReason?: String;
  }
  export type ModelPackageStatusItemList = ModelPackageStatusItem[];
  export type ModelPackageSummaries = {[key: string]: BatchDescribeModelPackageSummary};
  export interface ModelPackageSummary {
    /**
     * The name of the model package.
     */
    ModelPackageName?: EntityName;
    /**
     * If the model package is a versioned model, the model group that the versioned model belongs to.
     */
    ModelPackageGroupName?: EntityName;
    /**
     * If the model package is a versioned model, the version of the model.
     */
    ModelPackageVersion?: ModelPackageVersion;
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    ModelPackageArn: ModelPackageArn;
    /**
     * A brief description of the model package.
     */
    ModelPackageDescription?: EntityDescription;
    /**
     * A timestamp that shows when the model package was created.
     */
    CreationTime: CreationTime;
    /**
     * The overall status of the model package.
     */
    ModelPackageStatus: ModelPackageStatus;
    /**
     * The approval status of the model. This can be one of the following values.    APPROVED - The model is approved    REJECTED - The model is rejected.    PENDING_MANUAL_APPROVAL - The model is waiting for manual approval.  
     */
    ModelApprovalStatus?: ModelApprovalStatus;
  }
  export type ModelPackageSummaryList = ModelPackageSummary[];
  export type ModelPackageType = "Versioned"|"Unversioned"|"Both"|string;
  export interface ModelPackageValidationProfile {
    /**
     * The name of the profile for the model package.
     */
    ProfileName: EntityName;
    /**
     * The TransformJobDefinition object that describes the transform job used for the validation of the model package.
     */
    TransformJobDefinition: TransformJobDefinition;
  }
  export type ModelPackageValidationProfiles = ModelPackageValidationProfile[];
  export interface ModelPackageValidationSpecification {
    /**
     * The IAM roles to be used for the validation of the model package.
     */
    ValidationRole: RoleArn;
    /**
     * An array of ModelPackageValidationProfile objects, each of which specifies a batch transform job that SageMaker runs to validate your model package.
     */
    ValidationProfiles: ModelPackageValidationProfiles;
  }
  export type ModelPackageVersion = number;
  export interface ModelQuality {
    /**
     * Model quality statistics.
     */
    Statistics?: MetricsSource;
    /**
     * Model quality constraints.
     */
    Constraints?: MetricsSource;
  }
  export interface ModelQualityAppSpecification {
    /**
     * The address of the container image that the monitoring job runs.
     */
    ImageUri: ImageUri;
    /**
     * Specifies the entrypoint for a container that the monitoring job runs.
     */
    ContainerEntrypoint?: ContainerEntrypoint;
    /**
     * An array of arguments for the container used to run the monitoring job.
     */
    ContainerArguments?: MonitoringContainerArguments;
    /**
     * An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
     */
    RecordPreprocessorSourceUri?: S3Uri;
    /**
     * An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
     */
    PostAnalyticsProcessorSourceUri?: S3Uri;
    /**
     * The machine learning problem type of the model that the monitoring job monitors.
     */
    ProblemType?: MonitoringProblemType;
    /**
     * Sets the environment variables in the container that the monitoring job runs.
     */
    Environment?: MonitoringEnvironmentMap;
  }
  export interface ModelQualityBaselineConfig {
    /**
     * The name of the job that performs baselining for the monitoring job.
     */
    BaseliningJobName?: ProcessingJobName;
    ConstraintsResource?: MonitoringConstraintsResource;
  }
  export interface ModelQualityJobInput {
    EndpointInput?: EndpointInput;
    /**
     * Input object for the batch transform job.
     */
    BatchTransformInput?: BatchTransformInput;
    /**
     * The ground truth label provided for the model.
     */
    GroundTruthS3Input: MonitoringGroundTruthS3Input;
  }
  export interface ModelQuantizationConfig {
    /**
     * The URI of an LMI DLC in Amazon ECR. SageMaker uses this image to run the optimization.
     */
    Image?: OptimizationContainerImage;
    /**
     * Environment variables that override the default ones in the model container.
     */
    OverrideEnvironment?: OptimizationJobEnvironmentVariables;
  }
  export interface ModelRegisterSettings {
    /**
     * Describes whether the integration to the model registry is enabled or disabled in the Canvas application.
     */
    Status?: FeatureStatus;
    /**
     * The Amazon Resource Name (ARN) of the SageMaker model registry account. Required only to register model versions created by a different SageMaker Canvas Amazon Web Services account than the Amazon Web Services account in which SageMaker model registry is set up.
     */
    CrossAccountModelRegisterRoleArn?: RoleArn;
  }
  export type ModelSetupTime = number;
  export type ModelSortKey = "Name"|"CreationTime"|string;
  export interface ModelStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the created model.
     */
    Arn?: String256;
  }
  export interface ModelSummary {
    /**
     * The name of the model that you want a summary for.
     */
    ModelName: ModelName;
    /**
     * The Amazon Resource Name (ARN) of the model.
     */
    ModelArn: ModelArn;
    /**
     * A timestamp that indicates when the model was created.
     */
    CreationTime: Timestamp;
  }
  export type ModelSummaryList = ModelSummary[];
  export type ModelVariantAction = "Retain"|"Remove"|"Promote"|string;
  export type ModelVariantActionMap = {[key: string]: ModelVariantAction};
  export interface ModelVariantConfig {
    /**
     * The name of the Amazon SageMaker Model entity.
     */
    ModelName: ModelName;
    /**
     * The name of the variant.
     */
    VariantName: ModelVariantName;
    /**
     * The configuration for the infrastructure that the model will be deployed to.
     */
    InfrastructureConfig: ModelInfrastructureConfig;
  }
  export type ModelVariantConfigList = ModelVariantConfig[];
  export interface ModelVariantConfigSummary {
    /**
     * The name of the Amazon SageMaker Model entity.
     */
    ModelName: ModelName;
    /**
     * The name of the variant.
     */
    VariantName: ModelVariantName;
    /**
     * The configuration of the infrastructure that the model has been deployed to.
     */
    InfrastructureConfig: ModelInfrastructureConfig;
    /**
     * The status of deployment for the model variant on the hosted inference endpoint.    Creating - Amazon SageMaker is preparing the model variant on the hosted inference endpoint.     InService - The model variant is running on the hosted inference endpoint.     Updating - Amazon SageMaker is updating the model variant on the hosted inference endpoint.     Deleting - Amazon SageMaker is deleting the model variant on the hosted inference endpoint.     Deleted - The model variant has been deleted on the hosted inference endpoint. This can only happen after stopping the experiment.   
     */
    Status: ModelVariantStatus;
  }
  export type ModelVariantConfigSummaryList = ModelVariantConfigSummary[];
  export type ModelVariantName = string;
  export type ModelVariantStatus = "Creating"|"Updating"|"InService"|"Deleting"|"Deleted"|string;
  export interface MonitoringAlertActions {
    /**
     * An alert action taken to light up an icon on the Model Dashboard when an alert goes into InAlert status.
     */
    ModelDashboardIndicator?: ModelDashboardIndicatorAction;
  }
  export type MonitoringAlertHistoryList = MonitoringAlertHistorySummary[];
  export type MonitoringAlertHistorySortKey = "CreationTime"|"Status"|string;
  export interface MonitoringAlertHistorySummary {
    /**
     * The name of a monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The name of a monitoring alert.
     */
    MonitoringAlertName: MonitoringAlertName;
    /**
     * A timestamp that indicates when the first alert transition occurred in an alert history. An alert transition can be from status InAlert to OK, or from OK to InAlert.
     */
    CreationTime: Timestamp;
    /**
     * The current alert status of an alert.
     */
    AlertStatus: MonitoringAlertStatus;
  }
  export type MonitoringAlertName = string;
  export type MonitoringAlertStatus = "InAlert"|"OK"|string;
  export interface MonitoringAlertSummary {
    /**
     * The name of a monitoring alert.
     */
    MonitoringAlertName: MonitoringAlertName;
    /**
     * A timestamp that indicates when a monitor alert was created.
     */
    CreationTime: Timestamp;
    /**
     * A timestamp that indicates when a monitor alert was last updated.
     */
    LastModifiedTime: Timestamp;
    /**
     * The current status of an alert.
     */
    AlertStatus: MonitoringAlertStatus;
    /**
     * Within EvaluationPeriod, how many execution failures will raise an alert.
     */
    DatapointsToAlert: MonitoringDatapointsToAlert;
    /**
     * The number of most recent monitoring executions to consider when evaluating alert status.
     */
    EvaluationPeriod: MonitoringEvaluationPeriod;
    /**
     * A list of alert actions taken in response to an alert going into InAlert status.
     */
    Actions: MonitoringAlertActions;
  }
  export type MonitoringAlertSummaryList = MonitoringAlertSummary[];
  export interface MonitoringAppSpecification {
    /**
     * The container image to be run by the monitoring job.
     */
    ImageUri: ImageUri;
    /**
     * Specifies the entrypoint for a container used to run the monitoring job.
     */
    ContainerEntrypoint?: ContainerEntrypoint;
    /**
     * An array of arguments for the container used to run the monitoring job.
     */
    ContainerArguments?: MonitoringContainerArguments;
    /**
     * An Amazon S3 URI to a script that is called per row prior to running analysis. It can base64 decode the payload and convert it into a flattened JSON so that the built-in container can use the converted data. Applicable only for the built-in (first party) containers.
     */
    RecordPreprocessorSourceUri?: S3Uri;
    /**
     * An Amazon S3 URI to a script that is called after analysis has been performed. Applicable only for the built-in (first party) containers.
     */
    PostAnalyticsProcessorSourceUri?: S3Uri;
  }
  export interface MonitoringBaselineConfig {
    /**
     * The name of the job that performs baselining for the monitoring job.
     */
    BaseliningJobName?: ProcessingJobName;
    /**
     * The baseline constraint file in Amazon S3 that the current monitoring job should validated against.
     */
    ConstraintsResource?: MonitoringConstraintsResource;
    /**
     * The baseline statistics file in Amazon S3 that the current monitoring job should be validated against.
     */
    StatisticsResource?: MonitoringStatisticsResource;
  }
  export interface MonitoringClusterConfig {
    /**
     * The number of ML compute instances to use in the model monitoring job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
     */
    InstanceCount: ProcessingInstanceCount;
    /**
     * The ML compute instance type for the processing job.
     */
    InstanceType: ProcessingInstanceType;
    /**
     * The size of the ML storage volume, in gigabytes, that you want to provision. You must specify sufficient ML storage for your scenario.
     */
    VolumeSizeInGB: ProcessingVolumeSizeInGB;
    /**
     * The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the model monitoring job.
     */
    VolumeKmsKeyId?: KmsKeyId;
  }
  export interface MonitoringConstraintsResource {
    /**
     * The Amazon S3 URI for the constraints resource.
     */
    S3Uri?: S3Uri;
  }
  export type MonitoringContainerArguments = ContainerArgument[];
  export interface MonitoringCsvDatasetFormat {
    /**
     * Indicates if the CSV data has a header.
     */
    Header?: Boolean;
  }
  export type MonitoringDatapointsToAlert = number;
  export interface MonitoringDatasetFormat {
    /**
     * The CSV dataset used in the monitoring job.
     */
    Csv?: MonitoringCsvDatasetFormat;
    /**
     * The JSON dataset used in the monitoring job
     */
    Json?: MonitoringJsonDatasetFormat;
    /**
     * The Parquet dataset used in the monitoring job
     */
    Parquet?: MonitoringParquetDatasetFormat;
  }
  export type MonitoringEnvironmentMap = {[key: string]: ProcessingEnvironmentValue};
  export type MonitoringEvaluationPeriod = number;
  export type MonitoringExecutionSortKey = "CreationTime"|"ScheduledTime"|"Status"|string;
  export interface MonitoringExecutionSummary {
    /**
     * The name of the monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The time the monitoring job was scheduled.
     */
    ScheduledTime: Timestamp;
    /**
     * The time at which the monitoring job was created.
     */
    CreationTime: Timestamp;
    /**
     * A timestamp that indicates the last time the monitoring job was modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The status of the monitoring job.
     */
    MonitoringExecutionStatus: ExecutionStatus;
    /**
     * The Amazon Resource Name (ARN) of the monitoring job.
     */
    ProcessingJobArn?: ProcessingJobArn;
    /**
     * The name of the endpoint used to run the monitoring job.
     */
    EndpointName?: EndpointName;
    /**
     * Contains the reason a monitoring job failed, if it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The name of the monitoring job.
     */
    MonitoringJobDefinitionName?: MonitoringJobDefinitionName;
    /**
     * The type of the monitoring job.
     */
    MonitoringType?: MonitoringType;
  }
  export type MonitoringExecutionSummaryList = MonitoringExecutionSummary[];
  export interface MonitoringGroundTruthS3Input {
    /**
     * The address of the Amazon S3 location of the ground truth labels.
     */
    S3Uri?: MonitoringS3Uri;
  }
  export interface MonitoringInput {
    /**
     * The endpoint for a monitoring job.
     */
    EndpointInput?: EndpointInput;
    /**
     * Input object for the batch transform job.
     */
    BatchTransformInput?: BatchTransformInput;
  }
  export type MonitoringInputs = MonitoringInput[];
  export interface MonitoringJobDefinition {
    /**
     * Baseline configuration used to validate that the data conforms to the specified constraints and statistics
     */
    BaselineConfig?: MonitoringBaselineConfig;
    /**
     * The array of inputs for the monitoring job. Currently we support monitoring an Amazon SageMaker Endpoint.
     */
    MonitoringInputs: MonitoringInputs;
    /**
     * The array of outputs from the monitoring job to be uploaded to Amazon S3.
     */
    MonitoringOutputConfig: MonitoringOutputConfig;
    /**
     * Identifies the resources, ML compute instances, and ML storage volumes to deploy for a monitoring job. In distributed processing, you specify more than one instance.
     */
    MonitoringResources: MonitoringResources;
    /**
     * Configures the monitoring job to run a specified Docker container image.
     */
    MonitoringAppSpecification: MonitoringAppSpecification;
    /**
     * Specifies a time limit for how long the monitoring job is allowed to run.
     */
    StoppingCondition?: MonitoringStoppingCondition;
    /**
     * Sets the environment variables in the Docker container.
     */
    Environment?: MonitoringEnvironmentMap;
    /**
     * Specifies networking options for an monitoring job.
     */
    NetworkConfig?: NetworkConfig;
    /**
     * The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.
     */
    RoleArn: RoleArn;
  }
  export type MonitoringJobDefinitionArn = string;
  export type MonitoringJobDefinitionName = string;
  export type MonitoringJobDefinitionSortKey = "Name"|"CreationTime"|string;
  export interface MonitoringJobDefinitionSummary {
    /**
     * The name of the monitoring job.
     */
    MonitoringJobDefinitionName: MonitoringJobDefinitionName;
    /**
     * The Amazon Resource Name (ARN) of the monitoring job.
     */
    MonitoringJobDefinitionArn: MonitoringJobDefinitionArn;
    /**
     * The time that the monitoring job was created.
     */
    CreationTime: Timestamp;
    /**
     * The name of the endpoint that the job monitors.
     */
    EndpointName: EndpointName;
  }
  export type MonitoringJobDefinitionSummaryList = MonitoringJobDefinitionSummary[];
  export interface MonitoringJsonDatasetFormat {
    /**
     * Indicates if the file should be read as a JSON object per line. 
     */
    Line?: Boolean;
  }
  export type MonitoringMaxRuntimeInSeconds = number;
  export interface MonitoringNetworkConfig {
    /**
     * Whether to encrypt all communications between the instances used for the monitoring jobs. Choose True to encrypt communications. Encryption provides greater security for distributed jobs, but the processing might take longer.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * Whether to allow inbound and outbound network calls to and from the containers used for the monitoring job.
     */
    EnableNetworkIsolation?: Boolean;
    VpcConfig?: VpcConfig;
  }
  export interface MonitoringOutput {
    /**
     * The Amazon S3 storage location where the results of a monitoring job are saved.
     */
    S3Output: MonitoringS3Output;
  }
  export interface MonitoringOutputConfig {
    /**
     * Monitoring outputs for monitoring jobs. This is where the output of the periodic monitoring jobs is uploaded.
     */
    MonitoringOutputs: MonitoringOutputs;
    /**
     * The Key Management Service (KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.
     */
    KmsKeyId?: KmsKeyId;
  }
  export type MonitoringOutputs = MonitoringOutput[];
  export interface MonitoringParquetDatasetFormat {
  }
  export type MonitoringProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression"|string;
  export interface MonitoringResources {
    /**
     * The configuration for the cluster resources used to run the processing job.
     */
    ClusterConfig: MonitoringClusterConfig;
  }
  export interface MonitoringS3Output {
    /**
     * A URI that identifies the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job.
     */
    S3Uri: MonitoringS3Uri;
    /**
     * The local path to the Amazon S3 storage location where Amazon SageMaker saves the results of a monitoring job. LocalPath is an absolute path for the output data.
     */
    LocalPath: ProcessingLocalPath;
    /**
     * Whether to upload the results of the monitoring job continuously or after the job completes.
     */
    S3UploadMode?: ProcessingS3UploadMode;
  }
  export type MonitoringS3Uri = string;
  export interface MonitoringSchedule {
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn?: MonitoringScheduleArn;
    /**
     * The name of the monitoring schedule.
     */
    MonitoringScheduleName?: MonitoringScheduleName;
    /**
     * The status of the monitoring schedule. This can be one of the following values.    PENDING - The schedule is pending being created.    FAILED - The schedule failed.    SCHEDULED - The schedule was successfully created.    STOPPED - The schedule was stopped.  
     */
    MonitoringScheduleStatus?: ScheduleStatus;
    /**
     * The type of the monitoring job definition to schedule.
     */
    MonitoringType?: MonitoringType;
    /**
     * If the monitoring schedule failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The time that the monitoring schedule was created.
     */
    CreationTime?: Timestamp;
    /**
     * The last time the monitoring schedule was changed.
     */
    LastModifiedTime?: Timestamp;
    MonitoringScheduleConfig?: MonitoringScheduleConfig;
    /**
     * The endpoint that hosts the model being monitored.
     */
    EndpointName?: EndpointName;
    LastMonitoringExecutionSummary?: MonitoringExecutionSummary;
    /**
     * A list of the tags associated with the monitoring schedlue. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.
     */
    Tags?: TagList;
  }
  export type MonitoringScheduleArn = string;
  export interface MonitoringScheduleConfig {
    /**
     * Configures the monitoring schedule.
     */
    ScheduleConfig?: ScheduleConfig;
    /**
     * Defines the monitoring job.
     */
    MonitoringJobDefinition?: MonitoringJobDefinition;
    /**
     * The name of the monitoring job definition to schedule.
     */
    MonitoringJobDefinitionName?: MonitoringJobDefinitionName;
    /**
     * The type of the monitoring job definition to schedule.
     */
    MonitoringType?: MonitoringType;
  }
  export type MonitoringScheduleList = MonitoringSchedule[];
  export type MonitoringScheduleName = string;
  export type MonitoringScheduleSortKey = "Name"|"CreationTime"|"Status"|string;
  export interface MonitoringScheduleSummary {
    /**
     * The name of the monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn: MonitoringScheduleArn;
    /**
     * The creation time of the monitoring schedule.
     */
    CreationTime: Timestamp;
    /**
     * The last time the monitoring schedule was modified.
     */
    LastModifiedTime: Timestamp;
    /**
     * The status of the monitoring schedule.
     */
    MonitoringScheduleStatus: ScheduleStatus;
    /**
     * The name of the endpoint using the monitoring schedule.
     */
    EndpointName?: EndpointName;
    /**
     * The name of the monitoring job definition that the schedule is for.
     */
    MonitoringJobDefinitionName?: MonitoringJobDefinitionName;
    /**
     * The type of the monitoring job definition that the schedule is for.
     */
    MonitoringType?: MonitoringType;
  }
  export type MonitoringScheduleSummaryList = MonitoringScheduleSummary[];
  export interface MonitoringStatisticsResource {
    /**
     * The Amazon S3 URI for the statistics resource.
     */
    S3Uri?: S3Uri;
  }
  export interface MonitoringStoppingCondition {
    /**
     * The maximum runtime allowed in seconds.  The MaxRuntimeInSeconds cannot exceed the frequency of the job. For data quality and model explainability, this can be up to 3600 seconds for an hourly schedule. For model bias and model quality hourly schedules, this can be up to 1800 seconds. 
     */
    MaxRuntimeInSeconds: MonitoringMaxRuntimeInSeconds;
  }
  export type MonitoringTimeOffsetString = string;
  export type MonitoringType = "DataQuality"|"ModelQuality"|"ModelBias"|"ModelExplainability"|string;
  export type MountPath = string;
  export interface MultiModelConfig {
    /**
     * Whether to cache models for a multi-model endpoint. By default, multi-model endpoints cache models so that a model does not have to be loaded into memory each time it is invoked. Some use cases do not benefit from model caching. For example, if an endpoint hosts a large number of models that are each invoked infrequently, the endpoint might perform better if you disable model caching. To disable model caching, set the value of this parameter to Disabled.
     */
    ModelCacheSetting?: ModelCacheSetting;
  }
  export type NameContains = string;
  export interface NeoVpcConfig {
    /**
     * The VPC security group IDs. IDs have the form of sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
     */
    SecurityGroupIds: NeoVpcSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC that you want to connect the compilation job to for accessing the model in Amazon S3.
     */
    Subnets: NeoVpcSubnets;
  }
  export type NeoVpcSecurityGroupId = string;
  export type NeoVpcSecurityGroupIds = NeoVpcSecurityGroupId[];
  export type NeoVpcSubnetId = string;
  export type NeoVpcSubnets = NeoVpcSubnetId[];
  export interface NestedFilters {
    /**
     * The name of the property to use in the nested filters. The value must match a listed property name, such as InputDataConfig.
     */
    NestedPropertyName: ResourcePropertyName;
    /**
     * A list of filters. Each filter acts on a property. Filters must contain at least one Filters value. For example, a NestedFilters call might include a filter on the PropertyName parameter of the InputDataConfig property: InputDataConfig.DataSource.S3DataSource.S3Uri.
     */
    Filters: FilterList;
  }
  export type NestedFiltersList = NestedFilters[];
  export interface NetworkConfig {
    /**
     * Whether to encrypt all communications between distributed processing jobs. Choose True to encrypt communications. Encryption provides greater security for distributed processing jobs, but the processing might take longer.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * Whether to allow inbound and outbound network calls to and from the containers used for the processing job.
     */
    EnableNetworkIsolation?: Boolean;
    VpcConfig?: VpcConfig;
  }
  export type NetworkInterfaceId = string;
  export type NextToken = string;
  export type NonEmptyString256 = string;
  export type NonEmptyString64 = string;
  export type NotebookInstanceAcceleratorType = "ml.eia1.medium"|"ml.eia1.large"|"ml.eia1.xlarge"|"ml.eia2.medium"|"ml.eia2.large"|"ml.eia2.xlarge"|string;
  export type NotebookInstanceAcceleratorTypes = NotebookInstanceAcceleratorType[];
  export type NotebookInstanceArn = string;
  export type NotebookInstanceLifecycleConfigArn = string;
  export type NotebookInstanceLifecycleConfigContent = string;
  export type NotebookInstanceLifecycleConfigList = NotebookInstanceLifecycleHook[];
  export type NotebookInstanceLifecycleConfigName = string;
  export type NotebookInstanceLifecycleConfigNameContains = string;
  export type NotebookInstanceLifecycleConfigSortKey = "Name"|"CreationTime"|"LastModifiedTime"|string;
  export type NotebookInstanceLifecycleConfigSortOrder = "Ascending"|"Descending"|string;
  export interface NotebookInstanceLifecycleConfigSummary {
    /**
     * The name of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName;
    /**
     * The Amazon Resource Name (ARN) of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigArn: NotebookInstanceLifecycleConfigArn;
    /**
     * A timestamp that tells when the lifecycle configuration was created.
     */
    CreationTime?: CreationTime;
    /**
     * A timestamp that tells when the lifecycle configuration was last modified.
     */
    LastModifiedTime?: LastModifiedTime;
  }
  export type NotebookInstanceLifecycleConfigSummaryList = NotebookInstanceLifecycleConfigSummary[];
  export interface NotebookInstanceLifecycleHook {
    /**
     * A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.
     */
    Content?: NotebookInstanceLifecycleConfigContent;
  }
  export type NotebookInstanceName = string;
  export type NotebookInstanceNameContains = string;
  export type NotebookInstanceSortKey = "Name"|"CreationTime"|"Status"|string;
  export type NotebookInstanceSortOrder = "Ascending"|"Descending"|string;
  export type NotebookInstanceStatus = "Pending"|"InService"|"Stopping"|"Stopped"|"Failed"|"Deleting"|"Updating"|string;
  export interface NotebookInstanceSummary {
    /**
     * The name of the notebook instance that you want a summary for.
     */
    NotebookInstanceName: NotebookInstanceName;
    /**
     * The Amazon Resource Name (ARN) of the notebook instance.
     */
    NotebookInstanceArn: NotebookInstanceArn;
    /**
     * The status of the notebook instance.
     */
    NotebookInstanceStatus?: NotebookInstanceStatus;
    /**
     * The URL that you use to connect to the Jupyter notebook running in your notebook instance. 
     */
    Url?: NotebookInstanceUrl;
    /**
     * The type of ML compute instance that the notebook instance is running on.
     */
    InstanceType?: InstanceType;
    /**
     * A timestamp that shows when the notebook instance was created.
     */
    CreationTime?: CreationTime;
    /**
     * A timestamp that shows when the notebook instance was last modified.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The name of a notebook instance lifecycle configuration associated with this notebook instance. For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
     */
    NotebookInstanceLifecycleConfigName?: NotebookInstanceLifecycleConfigName;
    /**
     * The Git repository associated with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    DefaultCodeRepository?: CodeRepositoryNameOrUrl;
    /**
     * An array of up to three Git repositories associated with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    AdditionalCodeRepositories?: AdditionalCodeRepositoryNamesOrUrls;
  }
  export type NotebookInstanceSummaryList = NotebookInstanceSummary[];
  export type NotebookInstanceUrl = string;
  export type NotebookInstanceVolumeSizeInGB = number;
  export type NotebookOutputOption = "Allowed"|"Disabled"|string;
  export interface NotificationConfiguration {
    /**
     * The ARN for the Amazon SNS topic to which notifications should be published.
     */
    NotificationTopicArn?: NotificationTopicArn;
  }
  export type NotificationTopicArn = string;
  export type NumberOfAcceleratorDevices = number;
  export type NumberOfCpuCores = number;
  export type NumberOfHumanWorkersPerDataObject = number;
  export type NumberOfSteps = number;
  export type ObjectiveStatus = "Succeeded"|"Pending"|"Failed"|string;
  export type ObjectiveStatusCounter = number;
  export interface ObjectiveStatusCounters {
    /**
     * The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.
     */
    Succeeded?: ObjectiveStatusCounter;
    /**
     * The number of training jobs that are in progress and pending evaluation of their final objective metric.
     */
    Pending?: ObjectiveStatusCounter;
    /**
     * The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.
     */
    Failed?: ObjectiveStatusCounter;
  }
  export interface OfflineStoreConfig {
    /**
     * The Amazon Simple Storage (Amazon S3) location of OfflineStore.
     */
    S3StorageConfig: S3StorageConfig;
    /**
     * Set to True to disable the automatic creation of an Amazon Web Services Glue table when configuring an OfflineStore. If set to False, Feature Store will name the OfflineStore Glue table following Athena's naming recommendations. The default value is False.
     */
    DisableGlueTableCreation?: Boolean;
    /**
     * The meta data of the Glue table that is autogenerated when an OfflineStore is created. 
     */
    DataCatalogConfig?: DataCatalogConfig;
    /**
     * Format for the offline store table. Supported formats are Glue (Default) and Apache Iceberg.
     */
    TableFormat?: TableFormat;
  }
  export interface OfflineStoreStatus {
    /**
     * An OfflineStore status.
     */
    Status: OfflineStoreStatusValue;
    /**
     * The justification for why the OfflineStoreStatus is Blocked (if applicable).
     */
    BlockedReason?: BlockedReason;
  }
  export type OfflineStoreStatusValue = "Active"|"Blocked"|"Disabled"|string;
  export interface OidcConfig {
    /**
     * The OIDC IdP client ID used to configure your private workforce.
     */
    ClientId: ClientId;
    /**
     * The OIDC IdP client secret used to configure your private workforce.
     */
    ClientSecret: ClientSecret;
    /**
     * The OIDC IdP issuer used to configure your private workforce.
     */
    Issuer: OidcEndpoint;
    /**
     * The OIDC IdP authorization endpoint used to configure your private workforce.
     */
    AuthorizationEndpoint: OidcEndpoint;
    /**
     * The OIDC IdP token endpoint used to configure your private workforce.
     */
    TokenEndpoint: OidcEndpoint;
    /**
     * The OIDC IdP user information endpoint used to configure your private workforce.
     */
    UserInfoEndpoint: OidcEndpoint;
    /**
     * The OIDC IdP logout endpoint used to configure your private workforce.
     */
    LogoutEndpoint: OidcEndpoint;
    /**
     * The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
     */
    JwksUri: OidcEndpoint;
    /**
     * An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
     */
    Scope?: Scope;
    /**
     * A string to string map of identifiers specific to the custom identity provider (IdP) being used.
     */
    AuthenticationRequestExtraParams?: AuthenticationRequestExtraParams;
  }
  export interface OidcConfigForResponse {
    /**
     * The OIDC IdP client ID used to configure your private workforce.
     */
    ClientId?: ClientId;
    /**
     * The OIDC IdP issuer used to configure your private workforce.
     */
    Issuer?: OidcEndpoint;
    /**
     * The OIDC IdP authorization endpoint used to configure your private workforce.
     */
    AuthorizationEndpoint?: OidcEndpoint;
    /**
     * The OIDC IdP token endpoint used to configure your private workforce.
     */
    TokenEndpoint?: OidcEndpoint;
    /**
     * The OIDC IdP user information endpoint used to configure your private workforce.
     */
    UserInfoEndpoint?: OidcEndpoint;
    /**
     * The OIDC IdP logout endpoint used to configure your private workforce.
     */
    LogoutEndpoint?: OidcEndpoint;
    /**
     * The OIDC IdP JSON Web Key Set (Jwks) URI used to configure your private workforce.
     */
    JwksUri?: OidcEndpoint;
    /**
     * An array of string identifiers used to refer to the specific pieces of user data or claims that the client application wants to access.
     */
    Scope?: Scope;
    /**
     * A string to string map of identifiers specific to the custom identity provider (IdP) being used.
     */
    AuthenticationRequestExtraParams?: AuthenticationRequestExtraParams;
  }
  export type OidcEndpoint = string;
  export interface OidcMemberDefinition {
    /**
     * A list of comma seperated strings that identifies user groups in your OIDC IdP. Each user group is made up of a group of private workers.
     */
    Groups?: Groups;
  }
  export interface OnlineStoreConfig {
    /**
     * Use to specify KMS Key ID (KMSKeyId) for at-rest encryption of your OnlineStore.
     */
    SecurityConfig?: OnlineStoreSecurityConfig;
    /**
     * Turn OnlineStore off by specifying False for the EnableOnlineStore flag. Turn OnlineStore on by specifying True for the EnableOnlineStore flag.  The default value is False.
     */
    EnableOnlineStore?: Boolean;
    /**
     * Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
     */
    TtlDuration?: TtlDuration;
    /**
     * Option for different tiers of low latency storage for real-time data retrieval.    Standard: A managed low latency data store for feature groups.    InMemory: A managed data store for feature groups that supports very low latency retrieval.   
     */
    StorageType?: StorageType;
  }
  export interface OnlineStoreConfigUpdate {
    /**
     * Time to live duration, where the record is hard deleted after the expiration time is reached; ExpiresAt = EventTime + TtlDuration. For information on HardDelete, see the DeleteRecord API in the Amazon SageMaker API Reference guide.
     */
    TtlDuration?: TtlDuration;
  }
  export interface OnlineStoreSecurityConfig {
    /**
     * The Amazon Web Services Key Management Service (KMS) key ARN that SageMaker Feature Store uses to encrypt the Amazon S3 objects at rest using Amazon S3 server-side encryption. The caller (either user or IAM role) of CreateFeatureGroup must have below permissions to the OnlineStore KmsKeyId:    "kms:Encrypt"     "kms:Decrypt"     "kms:DescribeKey"     "kms:CreateGrant"     "kms:RetireGrant"     "kms:ReEncryptFrom"     "kms:ReEncryptTo"     "kms:GenerateDataKey"     "kms:ListAliases"     "kms:ListGrants"     "kms:RevokeGrant"    The caller (either user or IAM role) to all DataPlane operations (PutRecord, GetRecord, DeleteRecord) must have the following permissions to the KmsKeyId:    "kms:Decrypt"   
     */
    KmsKeyId?: KmsKeyId;
  }
  export type OnlineStoreTotalSizeBytes = number;
  export type Operator = "Equals"|"NotEquals"|"GreaterThan"|"GreaterThanOrEqualTo"|"LessThan"|"LessThanOrEqualTo"|"Contains"|"Exists"|"NotExists"|"In"|string;
  export interface OptimizationConfig {
    /**
     * Settings for the model quantization technique that's applied by a model optimization job.
     */
    ModelQuantizationConfig?: ModelQuantizationConfig;
    /**
     * Settings for the model compilation technique that's applied by a model optimization job.
     */
    ModelCompilationConfig?: ModelCompilationConfig;
  }
  export type OptimizationConfigs = OptimizationConfig[];
  export type OptimizationContainerImage = string;
  export type OptimizationJobArn = string;
  export type OptimizationJobDeploymentInstanceType = "ml.p4d.24xlarge"|"ml.p4de.24xlarge"|"ml.p5.48xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.12xlarge"|"ml.g5.16xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.g6.xlarge"|"ml.g6.2xlarge"|"ml.g6.4xlarge"|"ml.g6.8xlarge"|"ml.g6.12xlarge"|"ml.g6.16xlarge"|"ml.g6.24xlarge"|"ml.g6.48xlarge"|"ml.inf2.xlarge"|"ml.inf2.8xlarge"|"ml.inf2.24xlarge"|"ml.inf2.48xlarge"|"ml.trn1.2xlarge"|"ml.trn1.32xlarge"|"ml.trn1n.32xlarge"|string;
  export type OptimizationJobEnvironmentVariables = {[key: string]: String256};
  export interface OptimizationJobModelSource {
    /**
     * The Amazon S3 location of a source model to optimize with an optimization job.
     */
    S3?: OptimizationJobModelSourceS3;
  }
  export interface OptimizationJobModelSourceS3 {
    /**
     * An Amazon S3 URI that locates a source model to optimize with an optimization job.
     */
    S3Uri?: S3Uri;
    /**
     * The access configuration settings for the source ML model for an optimization job, where you can accept the model end-user license agreement (EULA).
     */
    ModelAccessConfig?: OptimizationModelAccessConfig;
  }
  export interface OptimizationJobOutputConfig {
    /**
     * The Amazon Resource Name (ARN) of a key in Amazon Web Services KMS. SageMaker uses they key to encrypt the artifacts of the optimized model when SageMaker uploads the model to Amazon S3.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The Amazon S3 URI for where to store the optimized model that you create with an optimization job.
     */
    S3OutputLocation: S3Uri;
  }
  export type OptimizationJobStatus = "INPROGRESS"|"COMPLETED"|"FAILED"|"STARTING"|"STOPPING"|"STOPPED"|string;
  export type OptimizationJobSummaries = OptimizationJobSummary[];
  export interface OptimizationJobSummary {
    /**
     * The name that you assigned to the optimization job.
     */
    OptimizationJobName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the optimization job.
     */
    OptimizationJobArn: OptimizationJobArn;
    /**
     * The time when you created the optimization job.
     */
    CreationTime: CreationTime;
    /**
     * The current status of the optimization job.
     */
    OptimizationJobStatus: OptimizationJobStatus;
    /**
     * The time when the optimization job started.
     */
    OptimizationStartTime?: Timestamp;
    /**
     * The time when the optimization job finished processing.
     */
    OptimizationEndTime?: Timestamp;
    /**
     * The time when the optimization job was last updated.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * The type of instance that hosts the optimized model that you create with the optimization job.
     */
    DeploymentInstanceType: OptimizationJobDeploymentInstanceType;
    /**
     * The optimization techniques that are applied by the optimization job.
     */
    OptimizationTypes: OptimizationTypes;
  }
  export type OptimizationModelAcceptEula = boolean;
  export interface OptimizationModelAccessConfig {
    /**
     * Specifies agreement to the model end-user license agreement (EULA). The AcceptEula value must be explicitly defined as True in order to accept the EULA that this model requires. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
     */
    AcceptEula: OptimizationModelAcceptEula;
  }
  export interface OptimizationOutput {
    /**
     * The image that SageMaker recommends that you use to host the optimized model that you created with an optimization job.
     */
    RecommendedInferenceImage?: OptimizationContainerImage;
  }
  export type OptimizationType = string;
  export type OptimizationTypes = OptimizationType[];
  export interface OptimizationVpcConfig {
    /**
     * The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
     */
    SecurityGroupIds: OptimizationVpcSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC to which you want to connect your optimized model.
     */
    Subnets: OptimizationVpcSubnets;
  }
  export type OptimizationVpcSecurityGroupId = string;
  export type OptimizationVpcSecurityGroupIds = OptimizationVpcSecurityGroupId[];
  export type OptimizationVpcSubnetId = string;
  export type OptimizationVpcSubnets = OptimizationVpcSubnetId[];
  export type OptionalDouble = number;
  export type OptionalInteger = number;
  export type OptionalVolumeSizeInGB = number;
  export type OrderKey = "Ascending"|"Descending"|string;
  export type OutputCompressionType = "GZIP"|"NONE"|string;
  export interface OutputConfig {
    /**
     * Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
     */
    S3OutputLocation: S3Uri;
    /**
     * Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.  Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region. 
     */
    TargetDevice?: TargetDevice;
    /**
     * Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice. The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:    Raspberry Pi 3 Model B+  "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},    "CompilerOptions": {'mattr': ['+neon']}    Jetson TX2  "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},    "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}    EC2 m5.2xlarge instance OS  "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},    "CompilerOptions": {'mcpu': 'skylake-avx512'}    RK3399  "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}    ARMv7 phone (CPU)  "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},    "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}    ARMv8 phone (CPU)  "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},    "CompilerOptions": {'ANDROID_PLATFORM': 29}   
     */
    TargetPlatform?: TargetPlatform;
    /**
     * Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.     DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:   float32: Use either "float" or "float32".   int64: Use either "int64" or "long".    For example, {"dtype" : "float32"}.    CPU: Compilation for CPU supports the following compiler options.    mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}     mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}       ARM: Details of ARM CPU compilations.    NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors. For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.      NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.    gpu_code: Specifies the targeted architecture.    trt-ver: Specifies the TensorRT versions in x.y.z. format.    cuda-ver: Specifies the CUDA version in x.y format.   For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}     ANDROID: Compilation for the Android OS supports the following compiler options:    ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.    mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.      INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".  For information about supported compiler options, see  Neuron Compiler CLI Reference Guide.     CoreML: Compilation for the CoreML OutputConfig TargetDevice supports the following compiler options:    class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.      EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:    precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".    signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.    output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.   For example: {"precision_mode": "FP32", "output_names": ["output:0"]}   
     */
    CompilerOptions?: CompilerOptions;
    /**
     * The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.  The KmsKeyId can be any of the following formats:    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias   
     */
    KmsKeyId?: KmsKeyId;
  }
  export interface OutputDataConfig {
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:    // KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    // Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"    // KMS Key Alias  "alias/ExampleAlias"    // Amazon Resource Name (ARN) of a KMS Key Alias  "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"    If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide. If the output data is stored in Amazon S3 Express One Zone, it is encrypted with server-side encryption with Amazon S3 managed keys (SSE-S3). KMS key is not supported for Amazon S3 Express One Zone The KMS key policy must grant permission to the IAM role that you specify in your CreateTrainingJob, CreateTransformJob, or CreateHyperParameterTuningJob requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * Identifies the S3 path where you want SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix. 
     */
    S3OutputPath: S3Uri;
    /**
     * The model output compression type. Select None to output an uncompressed model, recommended for large model outputs. Defaults to gzip.
     */
    CompressionType?: OutputCompressionType;
  }
  export interface OutputParameter {
    /**
     * The name of the output parameter.
     */
    Name: String256;
    /**
     * The value of the output parameter.
     */
    Value: String1024;
  }
  export type OutputParameterList = OutputParameter[];
  export interface OwnershipSettings {
    /**
     * The user profile who is the owner of the space.
     */
    OwnerUserProfileName: UserProfileName;
  }
  export interface OwnershipSettingsSummary {
    /**
     * The user profile who is the owner of the space.
     */
    OwnerUserProfileName?: UserProfileName;
  }
  export type PaginationToken = string;
  export interface ParallelismConfiguration {
    /**
     * The max number of steps that can be executed in parallel. 
     */
    MaxParallelExecutionSteps: MaxParallelExecutionSteps;
  }
  export interface Parameter {
    /**
     * The name of the parameter to assign a value to. This parameter name must match a named parameter in the pipeline definition.
     */
    Name: PipelineParameterName;
    /**
     * The literal value for the parameter.
     */
    Value: String1024;
  }
  export type ParameterKey = string;
  export type ParameterList = Parameter[];
  export type ParameterName = string;
  export interface ParameterRange {
    /**
     * A IntegerParameterRangeSpecification object that defines the possible values for an integer hyperparameter.
     */
    IntegerParameterRangeSpecification?: IntegerParameterRangeSpecification;
    /**
     * A ContinuousParameterRangeSpecification object that defines the possible values for a continuous hyperparameter.
     */
    ContinuousParameterRangeSpecification?: ContinuousParameterRangeSpecification;
    /**
     * A CategoricalParameterRangeSpecification object that defines the possible values for a categorical hyperparameter.
     */
    CategoricalParameterRangeSpecification?: CategoricalParameterRangeSpecification;
  }
  export interface ParameterRanges {
    /**
     * The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.
     */
    IntegerParameterRanges?: IntegerParameterRanges;
    /**
     * The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.
     */
    ContinuousParameterRanges?: ContinuousParameterRanges;
    /**
     * The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.
     */
    CategoricalParameterRanges?: CategoricalParameterRanges;
    /**
     * A list containing hyperparameter names and example values to be used by Autotune to determine optimal ranges for your tuning job.
     */
    AutoParameters?: AutoParameters;
  }
  export type ParameterType = "Integer"|"Continuous"|"Categorical"|"FreeText"|string;
  export type ParameterValue = string;
  export type ParameterValues = ParameterValue[];
  export interface Parent {
    /**
     * The name of the trial.
     */
    TrialName?: ExperimentEntityName;
    /**
     * The name of the experiment.
     */
    ExperimentName?: ExperimentEntityName;
  }
  export interface ParentHyperParameterTuningJob {
    /**
     * The name of the hyperparameter tuning job to be used as a starting point for a new hyperparameter tuning job.
     */
    HyperParameterTuningJobName?: HyperParameterTuningJobName;
  }
  export type ParentHyperParameterTuningJobs = ParentHyperParameterTuningJob[];
  export type Parents = Parent[];
  export interface PendingDeploymentSummary {
    /**
     * The name of the endpoint configuration used in the deployment. 
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint for the in-progress deployment.
     */
    ProductionVariants?: PendingProductionVariantSummaryList;
    /**
     * The start time of the deployment.
     */
    StartTime?: Timestamp;
    /**
     * An array of PendingProductionVariantSummary objects, one for each model hosted behind this endpoint in shadow mode with production traffic replicated from the model specified on ProductionVariants for the in-progress deployment.
     */
    ShadowProductionVariants?: PendingProductionVariantSummaryList;
  }
  export interface PendingProductionVariantSummary {
    /**
     * The name of the variant.
     */
    VariantName: VariantName;
    /**
     * An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.
     */
    DeployedImages?: DeployedImages;
    /**
     * The weight associated with the variant.
     */
    CurrentWeight?: VariantWeight;
    /**
     * The requested weight for the variant in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
     */
    DesiredWeight?: VariantWeight;
    /**
     * The number of instances associated with the variant.
     */
    CurrentInstanceCount?: TaskCount;
    /**
     * The number of instances requested in this deployment, as specified in the endpoint configuration for the endpoint. The value is taken from the request to the CreateEndpointConfig operation.
     */
    DesiredInstanceCount?: TaskCount;
    /**
     * The type of instances associated with the variant.
     */
    InstanceType?: ProductionVariantInstanceType;
    /**
     * The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
     */
    AcceleratorType?: ProductionVariantAcceleratorType;
    /**
     * The endpoint variant status which describes the current deployment stage status or operational status.
     */
    VariantStatus?: ProductionVariantStatusList;
    /**
     * The serverless configuration for the endpoint.
     */
    CurrentServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * The serverless configuration requested for this deployment, as specified in the endpoint configuration for the endpoint.
     */
    DesiredServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic. 
     */
    ManagedInstanceScaling?: ProductionVariantManagedInstanceScaling;
    /**
     * Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
     */
    RoutingConfig?: ProductionVariantRoutingConfig;
  }
  export type PendingProductionVariantSummaryList = PendingProductionVariantSummary[];
  export type Percentage = number;
  export interface Phase {
    /**
     * Specifies how many concurrent users to start with. The value should be between 1 and 3.
     */
    InitialNumberOfUsers?: InitialNumberOfUsers;
    /**
     * Specified how many new users to spawn in a minute.
     */
    SpawnRate?: SpawnRate;
    /**
     * Specifies how long a traffic phase should be. For custom load tests, the value should be between 120 and 3600. This value should not exceed JobDurationInSeconds.
     */
    DurationInSeconds?: TrafficDurationInSeconds;
  }
  export type Phases = Phase[];
  export interface Pipeline {
    /**
     * The Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineArn?: PipelineArn;
    /**
     * The name of the pipeline.
     */
    PipelineName?: PipelineName;
    /**
     * The display name of the pipeline.
     */
    PipelineDisplayName?: PipelineName;
    /**
     * The description of the pipeline.
     */
    PipelineDescription?: PipelineDescription;
    /**
     * The Amazon Resource Name (ARN) of the role that created the pipeline.
     */
    RoleArn?: RoleArn;
    /**
     * The status of the pipeline.
     */
    PipelineStatus?: PipelineStatus;
    /**
     * The creation time of the pipeline.
     */
    CreationTime?: Timestamp;
    /**
     * The time that the pipeline was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The time when the pipeline was last run.
     */
    LastRunTime?: Timestamp;
    CreatedBy?: UserContext;
    LastModifiedBy?: UserContext;
    /**
     * The parallelism configuration applied to the pipeline.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
    /**
     * A list of tags that apply to the pipeline.
     */
    Tags?: TagList;
  }
  export type PipelineArn = string;
  export type PipelineDefinition = string;
  export interface PipelineDefinitionS3Location {
    /**
     * Name of the S3 bucket.
     */
    Bucket: BucketName;
    /**
     * The object key (or key name) uniquely identifies the object in an S3 bucket. 
     */
    ObjectKey: Key;
    /**
     * Version Id of the pipeline definition file. If not specified, Amazon SageMaker will retrieve the latest version.
     */
    VersionId?: VersionId;
  }
  export type PipelineDescription = string;
  export interface PipelineExecution {
    /**
     * The Amazon Resource Name (ARN) of the pipeline that was executed.
     */
    PipelineArn?: PipelineArn;
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
    /**
     * The display name of the pipeline execution.
     */
    PipelineExecutionDisplayName?: PipelineExecutionName;
    /**
     * The status of the pipeline status.
     */
    PipelineExecutionStatus?: PipelineExecutionStatus;
    /**
     * The description of the pipeline execution.
     */
    PipelineExecutionDescription?: PipelineExecutionDescription;
    PipelineExperimentConfig?: PipelineExperimentConfig;
    /**
     * If the execution failed, a message describing why.
     */
    FailureReason?: PipelineExecutionFailureReason;
    /**
     * The creation time of the pipeline execution.
     */
    CreationTime?: Timestamp;
    /**
     * The time that the pipeline execution was last modified.
     */
    LastModifiedTime?: Timestamp;
    CreatedBy?: UserContext;
    LastModifiedBy?: UserContext;
    /**
     * The parallelism configuration applied to the pipeline execution.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
    /**
     * The selective execution configuration applied to the pipeline run.
     */
    SelectiveExecutionConfig?: SelectiveExecutionConfig;
    /**
     * Contains a list of pipeline parameters. This list can be empty. 
     */
    PipelineParameters?: ParameterList;
  }
  export type PipelineExecutionArn = string;
  export type PipelineExecutionDescription = string;
  export type PipelineExecutionFailureReason = string;
  export type PipelineExecutionName = string;
  export type PipelineExecutionStatus = "Executing"|"Stopping"|"Stopped"|"Failed"|"Succeeded"|string;
  export interface PipelineExecutionStep {
    /**
     * The name of the step that is executed.
     */
    StepName?: StepName;
    /**
     * The display name of the step.
     */
    StepDisplayName?: StepDisplayName;
    /**
     * The description of the step.
     */
    StepDescription?: StepDescription;
    /**
     * The time that the step started executing.
     */
    StartTime?: Timestamp;
    /**
     * The time that the step stopped executing.
     */
    EndTime?: Timestamp;
    /**
     * The status of the step execution.
     */
    StepStatus?: StepStatus;
    /**
     * If this pipeline execution step was cached, details on the cache hit.
     */
    CacheHitResult?: CacheHitResult;
    /**
     * The reason why the step failed execution. This is only returned if the step failed its execution.
     */
    FailureReason?: FailureReason;
    /**
     * Metadata to run the pipeline step.
     */
    Metadata?: PipelineExecutionStepMetadata;
    /**
     * The current attempt of the execution step. For more information, see Retry Policy for SageMaker Pipelines steps.
     */
    AttemptCount?: Integer;
    /**
     * The ARN from an execution of the current pipeline from which results are reused for this step.
     */
    SelectiveExecutionResult?: SelectiveExecutionResult;
  }
  export type PipelineExecutionStepList = PipelineExecutionStep[];
  export interface PipelineExecutionStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the training job that was run by this step execution.
     */
    TrainingJob?: TrainingJobStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the processing job that was run by this step execution.
     */
    ProcessingJob?: ProcessingJobStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
     */
    TransformJob?: TransformJobStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
     */
    TuningJob?: TuningJobStepMetaData;
    /**
     * The Amazon Resource Name (ARN) of the model that was created by this step execution.
     */
    Model?: ModelStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the model package that the model was registered to by this step execution.
     */
    RegisterModel?: RegisterModelStepMetadata;
    /**
     * The outcome of the condition evaluation that was run by this step execution.
     */
    Condition?: ConditionStepMetadata;
    /**
     * The URL of the Amazon SQS queue used by this step execution, the pipeline generated token, and a list of output parameters.
     */
    Callback?: CallbackStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the Lambda function that was run by this step execution and a list of output parameters.
     */
    Lambda?: LambdaStepMetadata;
    /**
     * The configurations and outcomes of an Amazon EMR step execution.
     */
    EMR?: EMRStepMetadata;
    /**
     * The configurations and outcomes of the check step execution. This includes:    The type of the check conducted.   The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.   The Amazon S3 URIs of newly calculated baseline constraints and statistics.   The model package group name provided.   The Amazon S3 URI of the violation report if violations detected.   The Amazon Resource Name (ARN) of check processing job initiated by the step execution.   The Boolean flags indicating if the drift check is skipped.   If step property BaselineUsedForDriftCheck is set the same as CalculatedBaseline.  
     */
    QualityCheck?: QualityCheckStepMetadata;
    /**
     * Container for the metadata for a Clarify check step. The configurations and outcomes of the check step execution. This includes:    The type of the check conducted,   The Amazon S3 URIs of baseline constraints and statistics files to be used for the drift check.   The Amazon S3 URIs of newly calculated baseline constraints and statistics.   The model package group name provided.   The Amazon S3 URI of the violation report if violations detected.   The Amazon Resource Name (ARN) of check processing job initiated by the step execution.   The boolean flags indicating if the drift check is skipped.   If step property BaselineUsedForDriftCheck is set the same as CalculatedBaseline.  
     */
    ClarifyCheck?: ClarifyCheckStepMetadata;
    /**
     * The configurations and outcomes of a Fail step execution.
     */
    Fail?: FailStepMetadata;
    /**
     * The Amazon Resource Name (ARN) of the AutoML job that was run by this step.
     */
    AutoMLJob?: AutoMLJobStepMetadata;
    /**
     * The endpoint that was invoked during this step execution.
     */
    Endpoint?: EndpointStepMetadata;
    /**
     * The endpoint configuration used to create an endpoint during this step execution.
     */
    EndpointConfig?: EndpointConfigStepMetadata;
  }
  export interface PipelineExecutionSummary {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
    /**
     * The start time of the pipeline execution.
     */
    StartTime?: Timestamp;
    /**
     * The status of the pipeline execution.
     */
    PipelineExecutionStatus?: PipelineExecutionStatus;
    /**
     * The description of the pipeline execution.
     */
    PipelineExecutionDescription?: PipelineExecutionDescription;
    /**
     * The display name of the pipeline execution.
     */
    PipelineExecutionDisplayName?: PipelineExecutionName;
    /**
     * A message generated by SageMaker Pipelines describing why the pipeline execution failed.
     */
    PipelineExecutionFailureReason?: String3072;
  }
  export type PipelineExecutionSummaryList = PipelineExecutionSummary[];
  export interface PipelineExperimentConfig {
    /**
     * The name of the experiment.
     */
    ExperimentName?: ExperimentEntityName;
    /**
     * The name of the trial.
     */
    TrialName?: ExperimentEntityName;
  }
  export type PipelineName = string;
  export type PipelineNameOrArn = string;
  export type PipelineParameterName = string;
  export type PipelineStatus = "Active"|"Deleting"|string;
  export interface PipelineSummary {
    /**
     *  The Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineArn?: PipelineArn;
    /**
     * The name of the pipeline.
     */
    PipelineName?: PipelineName;
    /**
     * The display name of the pipeline.
     */
    PipelineDisplayName?: PipelineName;
    /**
     * The description of the pipeline.
     */
    PipelineDescription?: PipelineDescription;
    /**
     * The Amazon Resource Name (ARN) that the pipeline used to execute.
     */
    RoleArn?: RoleArn;
    /**
     * The creation time of the pipeline.
     */
    CreationTime?: Timestamp;
    /**
     * The time that the pipeline was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The last time that a pipeline execution began.
     */
    LastExecutionTime?: Timestamp;
  }
  export type PipelineSummaryList = PipelineSummary[];
  export type PlatformIdentifier = string;
  export type PolicyString = string;
  export interface PredefinedMetricSpecification {
    /**
     * The metric type. You can only apply SageMaker metric types to SageMaker endpoints.
     */
    PredefinedMetricType?: String;
  }
  export type PresignedDomainUrl = string;
  export type ProbabilityThresholdAttribute = number;
  export type ProblemType = "BinaryClassification"|"MulticlassClassification"|"Regression"|string;
  export interface ProcessingClusterConfig {
    /**
     * The number of ML compute instances to use in the processing job. For distributed processing jobs, specify a value greater than 1. The default value is 1.
     */
    InstanceCount: ProcessingInstanceCount;
    /**
     * The ML compute instance type for the processing job.
     */
    InstanceType: ProcessingInstanceType;
    /**
     * The size of the ML storage volume in gigabytes that you want to provision. You must specify sufficient ML storage for your scenario.  Certain Nitro-based instances include local storage with a fixed total size, dependent on the instance type. When using these instances for processing, Amazon SageMaker mounts the local instance storage instead of Amazon EBS gp2 storage. You can't request a VolumeSizeInGB greater than the total size of the local instance storage. For a list of instance types that support local instance storage, including the total size per instance type, see Instance Store Volumes. 
     */
    VolumeSizeInGB: ProcessingVolumeSizeInGB;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the processing job.   Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes. 
     */
    VolumeKmsKeyId?: KmsKeyId;
  }
  export type ProcessingEnvironmentKey = string;
  export type ProcessingEnvironmentMap = {[key: string]: ProcessingEnvironmentValue};
  export type ProcessingEnvironmentValue = string;
  export interface ProcessingFeatureStoreOutput {
    /**
     * The name of the Amazon SageMaker FeatureGroup to use as the destination for processing job output. Note that your processing script is responsible for putting records into your Feature Store.
     */
    FeatureGroupName: FeatureGroupName;
  }
  export interface ProcessingInput {
    /**
     * The name for the processing job input.
     */
    InputName: String;
    /**
     * When True, input operations such as data download are managed natively by the processing job application. When False (default), input operations are managed by Amazon SageMaker.
     */
    AppManaged?: AppManaged;
    /**
     * Configuration for downloading input data from Amazon S3 into the processing container.
     */
    S3Input?: ProcessingS3Input;
    /**
     * Configuration for a Dataset Definition input. 
     */
    DatasetDefinition?: DatasetDefinition;
  }
  export type ProcessingInputs = ProcessingInput[];
  export type ProcessingInstanceCount = number;
  export type ProcessingInstanceType = "ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.r5d.large"|"ml.r5d.xlarge"|"ml.r5d.2xlarge"|"ml.r5d.4xlarge"|"ml.r5d.8xlarge"|"ml.r5d.12xlarge"|"ml.r5d.16xlarge"|"ml.r5d.24xlarge"|string;
  export interface ProcessingJob {
    /**
     * List of input configurations for the processing job.
     */
    ProcessingInputs?: ProcessingInputs;
    ProcessingOutputConfig?: ProcessingOutputConfig;
    /**
     * The name of the processing job.
     */
    ProcessingJobName?: ProcessingJobName;
    ProcessingResources?: ProcessingResources;
    StoppingCondition?: ProcessingStoppingCondition;
    AppSpecification?: AppSpecification;
    /**
     * Sets the environment variables in the Docker container.
     */
    Environment?: ProcessingEnvironmentMap;
    NetworkConfig?: NetworkConfig;
    /**
     * The ARN of the role used to create the processing job.
     */
    RoleArn?: RoleArn;
    ExperimentConfig?: ExperimentConfig;
    /**
     * The ARN of the processing job.
     */
    ProcessingJobArn?: ProcessingJobArn;
    /**
     * The status of the processing job.
     */
    ProcessingJobStatus?: ProcessingJobStatus;
    /**
     * A string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
     */
    ExitMessage?: ExitMessage;
    /**
     * A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The time that the processing job ended.
     */
    ProcessingEndTime?: Timestamp;
    /**
     * The time that the processing job started.
     */
    ProcessingStartTime?: Timestamp;
    /**
     * The time the processing job was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The time the processing job was created.
     */
    CreationTime?: Timestamp;
    /**
     * The ARN of a monitoring schedule for an endpoint associated with this processing job.
     */
    MonitoringScheduleArn?: MonitoringScheduleArn;
    /**
     * The Amazon Resource Name (ARN) of the AutoML job associated with this processing job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    /**
     * The ARN of the training job associated with this processing job.
     */
    TrainingJobArn?: TrainingJobArn;
    /**
     * An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services Billing and Cost Management User Guide.
     */
    Tags?: TagList;
  }
  export type ProcessingJobArn = string;
  export type ProcessingJobName = string;
  export type ProcessingJobStatus = "InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export interface ProcessingJobStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the processing job.
     */
    Arn?: ProcessingJobArn;
  }
  export type ProcessingJobSummaries = ProcessingJobSummary[];
  export interface ProcessingJobSummary {
    /**
     * The name of the processing job.
     */
    ProcessingJobName: ProcessingJobName;
    /**
     * The Amazon Resource Name (ARN) of the processing job..
     */
    ProcessingJobArn: ProcessingJobArn;
    /**
     * The time at which the processing job was created.
     */
    CreationTime: Timestamp;
    /**
     * The time at which the processing job completed.
     */
    ProcessingEndTime?: Timestamp;
    /**
     * A timestamp that indicates the last time the processing job was modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The status of the processing job.
     */
    ProcessingJobStatus: ProcessingJobStatus;
    /**
     * A string, up to one KB in size, that contains the reason a processing job failed, if it failed.
     */
    FailureReason?: FailureReason;
    /**
     * An optional string, up to one KB in size, that contains metadata from the processing container when the processing job exits.
     */
    ExitMessage?: ExitMessage;
  }
  export type ProcessingLocalPath = string;
  export type ProcessingMaxRuntimeInSeconds = number;
  export interface ProcessingOutput {
    /**
     * The name for the processing job output.
     */
    OutputName: String;
    /**
     * Configuration for processing job outputs in Amazon S3.
     */
    S3Output?: ProcessingS3Output;
    /**
     * Configuration for processing job outputs in Amazon SageMaker Feature Store. This processing output type is only supported when AppManaged is specified. 
     */
    FeatureStoreOutput?: ProcessingFeatureStoreOutput;
    /**
     * When True, output operations such as data upload are managed natively by the processing job application. When False (default), output operations are managed by Amazon SageMaker.
     */
    AppManaged?: AppManaged;
  }
  export interface ProcessingOutputConfig {
    /**
     * An array of outputs configuring the data to upload from the processing container.
     */
    Outputs: ProcessingOutputs;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the processing job output. KmsKeyId can be an ID of a KMS key, ARN of a KMS key, alias of a KMS key, or alias of a KMS key. The KmsKeyId is applied to all outputs.
     */
    KmsKeyId?: KmsKeyId;
  }
  export type ProcessingOutputs = ProcessingOutput[];
  export interface ProcessingResources {
    /**
     * The configuration for the resources in a cluster used to run the processing job.
     */
    ClusterConfig: ProcessingClusterConfig;
  }
  export type ProcessingS3CompressionType = "None"|"Gzip"|string;
  export type ProcessingS3DataDistributionType = "FullyReplicated"|"ShardedByS3Key"|string;
  export type ProcessingS3DataType = "ManifestFile"|"S3Prefix"|string;
  export interface ProcessingS3Input {
    /**
     * The URI of the Amazon S3 prefix Amazon SageMaker downloads data required to run a processing job.
     */
    S3Uri: S3Uri;
    /**
     * The local path in your container where you want Amazon SageMaker to write input data to. LocalPath is an absolute path to the input data and must begin with /opt/ml/processing/. LocalPath is a required parameter when AppManaged is False (default).
     */
    LocalPath?: ProcessingLocalPath;
    /**
     * Whether you use an S3Prefix or a ManifestFile for the data type. If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for the processing job. If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for the processing job.
     */
    S3DataType: ProcessingS3DataType;
    /**
     * Whether to use File or Pipe input mode. In File mode, Amazon SageMaker copies the data from the input source onto the local ML storage volume before starting your processing container. This is the most commonly used input mode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your processing container into named pipes without using the ML storage volume.
     */
    S3InputMode?: ProcessingS3InputMode;
    /**
     * Whether to distribute the data from Amazon S3 to all processing instances with FullyReplicated, or whether the data from Amazon S3 is shared by Amazon S3 key, downloading one shard of data to each processing instance.
     */
    S3DataDistributionType?: ProcessingS3DataDistributionType;
    /**
     * Whether to GZIP-decompress the data in Amazon S3 as it is streamed into the processing container. Gzip can only be used when Pipe mode is specified as the S3InputMode. In Pipe mode, Amazon SageMaker streams input data from the source directly to your container without using the EBS volume.
     */
    S3CompressionType?: ProcessingS3CompressionType;
  }
  export type ProcessingS3InputMode = "Pipe"|"File"|string;
  export interface ProcessingS3Output {
    /**
     * A URI that identifies the Amazon S3 bucket where you want Amazon SageMaker to save the results of a processing job.
     */
    S3Uri: S3Uri;
    /**
     * The local path of a directory where you want Amazon SageMaker to upload its contents to Amazon S3. LocalPath is an absolute path to a directory containing output files. This directory will be created by the platform and exist when your container's entrypoint is invoked.
     */
    LocalPath?: ProcessingLocalPath;
    /**
     * Whether to upload the results of the processing job continuously or after the job completes.
     */
    S3UploadMode: ProcessingS3UploadMode;
  }
  export type ProcessingS3UploadMode = "Continuous"|"EndOfJob"|string;
  export interface ProcessingStoppingCondition {
    /**
     * Specifies the maximum runtime in seconds.
     */
    MaxRuntimeInSeconds: ProcessingMaxRuntimeInSeconds;
  }
  export type ProcessingVolumeSizeInGB = number;
  export type Processor = "CPU"|"GPU"|string;
  export type ProductId = string;
  export type ProductListings = String[];
  export interface ProductionVariant {
    /**
     * The name of the production variant.
     */
    VariantName: VariantName;
    /**
     * The name of the model that you want to host. This is the name that you specified when creating the model.
     */
    ModelName?: ModelName;
    /**
     * Number of instances to launch initially.
     */
    InitialInstanceCount?: InitialTaskCount;
    /**
     * The ML compute instance type.
     */
    InstanceType?: ProductionVariantInstanceType;
    /**
     * Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0. 
     */
    InitialVariantWeight?: VariantWeight;
    /**
     * The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker.
     */
    AcceleratorType?: ProductionVariantAcceleratorType;
    /**
     * Specifies configuration for a core dump from the model container when the process crashes.
     */
    CoreDumpConfig?: ProductionVariantCoreDumpConfig;
    /**
     * The serverless configuration for an endpoint. Specifies a serverless endpoint configuration instead of an instance-based endpoint configuration.
     */
    ServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currently only Amazon EBS gp2 storage volumes are supported.
     */
    VolumeSizeInGB?: ProductionVariantVolumeSizeInGB;
    /**
     * The timeout value, in seconds, to download and extract the model that you want to host from Amazon S3 to the individual inference instance associated with this production variant.
     */
    ModelDataDownloadTimeoutInSeconds?: ProductionVariantModelDataDownloadTimeoutInSeconds;
    /**
     * The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check, see How Your Container Should Respond to Health Check (Ping) Requests.
     */
    ContainerStartupHealthCheckTimeoutInSeconds?: ProductionVariantContainerStartupHealthCheckTimeoutInSeconds;
    /**
     *  You can use this parameter to turn on native Amazon Web Services Systems Manager (SSM) access for a production variant behind an endpoint. By default, SSM access is disabled for all production variants behind an endpoint. You can turn on or turn off SSM access for a production variant behind an existing endpoint by creating a new endpoint configuration and calling UpdateEndpoint. 
     */
    EnableSSMAccess?: ProductionVariantSSMAccess;
    /**
     * Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic. 
     */
    ManagedInstanceScaling?: ProductionVariantManagedInstanceScaling;
    /**
     * Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
     */
    RoutingConfig?: ProductionVariantRoutingConfig;
    /**
     * Specifies an option from a collection of preconfigured Amazon Machine Image (AMI) images. Each image is configured by Amazon Web Services with a set of software and driver versions. Amazon Web Services optimizes these configurations for different machine learning workloads. By selecting an AMI version, you can ensure that your inference environment is compatible with specific software requirements, such as CUDA driver versions, Linux kernel versions, or Amazon Web Services Neuron driver versions. The AMI version names, and their configurations, are the following:  al2-ami-sagemaker-inference-gpu-2    Accelerator: GPU   NVIDIA driver version: 535.54.03   CUDA driver version: 12.2   Supported instance types: ml.g4dn.*, ml.g5.*, ml.g6.*, ml.p3.*, ml.p4d.*, ml.p4de.*, ml.p5.*    
     */
    InferenceAmiVersion?: ProductionVariantInferenceAmiVersion;
  }
  export type ProductionVariantAcceleratorType = "ml.eia1.medium"|"ml.eia1.large"|"ml.eia1.xlarge"|"ml.eia2.medium"|"ml.eia2.large"|"ml.eia2.xlarge"|string;
  export type ProductionVariantContainerStartupHealthCheckTimeoutInSeconds = number;
  export interface ProductionVariantCoreDumpConfig {
    /**
     * The Amazon S3 bucket to send the core dump to.
     */
    DestinationS3Uri: DestinationS3Uri;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that SageMaker uses to encrypt the core dump data at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:    // KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    // Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"    // KMS Key Alias  "alias/ExampleAlias"    // Amazon Resource Name (ARN) of a KMS Key Alias  "arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias"    If you use a KMS key ID or an alias of your KMS key, the SageMaker execution role must include permissions to call kms:Encrypt. If you don't provide a KMS key ID, SageMaker uses the default KMS key for Amazon S3 for your role's account. SageMaker uses server-side encryption with KMS-managed keys for OutputDataConfig. If you use a bucket policy with an s3:PutObject permission that only allows objects with server-side encryption, set the condition key of s3:x-amz-server-side-encryption to "aws:kms". For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.  The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint and UpdateEndpoint requests. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
     */
    KmsKeyId?: KmsKeyId;
  }
  export type ProductionVariantInferenceAmiVersion = "al2-ami-sagemaker-inference-gpu-2"|string;
  export type ProductionVariantInstanceType = "ml.t2.medium"|"ml.t2.large"|"ml.t2.xlarge"|"ml.t2.2xlarge"|"ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.m5d.large"|"ml.m5d.xlarge"|"ml.m5d.2xlarge"|"ml.m5d.4xlarge"|"ml.m5d.12xlarge"|"ml.m5d.24xlarge"|"ml.c4.large"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.large"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.c5d.large"|"ml.c5d.xlarge"|"ml.c5d.2xlarge"|"ml.c5d.4xlarge"|"ml.c5d.9xlarge"|"ml.c5d.18xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.12xlarge"|"ml.r5.24xlarge"|"ml.r5d.large"|"ml.r5d.xlarge"|"ml.r5d.2xlarge"|"ml.r5d.4xlarge"|"ml.r5d.12xlarge"|"ml.r5d.24xlarge"|"ml.inf1.xlarge"|"ml.inf1.2xlarge"|"ml.inf1.6xlarge"|"ml.inf1.24xlarge"|"ml.dl1.24xlarge"|"ml.c6i.large"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.12xlarge"|"ml.g5.16xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.g6.xlarge"|"ml.g6.2xlarge"|"ml.g6.4xlarge"|"ml.g6.8xlarge"|"ml.g6.12xlarge"|"ml.g6.16xlarge"|"ml.g6.24xlarge"|"ml.g6.48xlarge"|"ml.p4d.24xlarge"|"ml.c7g.large"|"ml.c7g.xlarge"|"ml.c7g.2xlarge"|"ml.c7g.4xlarge"|"ml.c7g.8xlarge"|"ml.c7g.12xlarge"|"ml.c7g.16xlarge"|"ml.m6g.large"|"ml.m6g.xlarge"|"ml.m6g.2xlarge"|"ml.m6g.4xlarge"|"ml.m6g.8xlarge"|"ml.m6g.12xlarge"|"ml.m6g.16xlarge"|"ml.m6gd.large"|"ml.m6gd.xlarge"|"ml.m6gd.2xlarge"|"ml.m6gd.4xlarge"|"ml.m6gd.8xlarge"|"ml.m6gd.12xlarge"|"ml.m6gd.16xlarge"|"ml.c6g.large"|"ml.c6g.xlarge"|"ml.c6g.2xlarge"|"ml.c6g.4xlarge"|"ml.c6g.8xlarge"|"ml.c6g.12xlarge"|"ml.c6g.16xlarge"|"ml.c6gd.large"|"ml.c6gd.xlarge"|"ml.c6gd.2xlarge"|"ml.c6gd.4xlarge"|"ml.c6gd.8xlarge"|"ml.c6gd.12xlarge"|"ml.c6gd.16xlarge"|"ml.c6gn.large"|"ml.c6gn.xlarge"|"ml.c6gn.2xlarge"|"ml.c6gn.4xlarge"|"ml.c6gn.8xlarge"|"ml.c6gn.12xlarge"|"ml.c6gn.16xlarge"|"ml.r6g.large"|"ml.r6g.xlarge"|"ml.r6g.2xlarge"|"ml.r6g.4xlarge"|"ml.r6g.8xlarge"|"ml.r6g.12xlarge"|"ml.r6g.16xlarge"|"ml.r6gd.large"|"ml.r6gd.xlarge"|"ml.r6gd.2xlarge"|"ml.r6gd.4xlarge"|"ml.r6gd.8xlarge"|"ml.r6gd.12xlarge"|"ml.r6gd.16xlarge"|"ml.p4de.24xlarge"|"ml.trn1.2xlarge"|"ml.trn1.32xlarge"|"ml.trn1n.32xlarge"|"ml.inf2.xlarge"|"ml.inf2.8xlarge"|"ml.inf2.24xlarge"|"ml.inf2.48xlarge"|"ml.p5.48xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|string;
  export type ProductionVariantList = ProductionVariant[];
  export interface ProductionVariantManagedInstanceScaling {
    /**
     * Indicates whether managed instance scaling is enabled.
     */
    Status?: ManagedInstanceScalingStatus;
    /**
     * The minimum number of instances that the endpoint must retain when it scales down to accommodate a decrease in traffic.
     */
    MinInstanceCount?: ManagedInstanceScalingMinInstanceCount;
    /**
     * The maximum number of instances that the endpoint can provision when it scales up to accommodate an increase in traffic.
     */
    MaxInstanceCount?: ManagedInstanceScalingMaxInstanceCount;
  }
  export type ProductionVariantModelDataDownloadTimeoutInSeconds = number;
  export interface ProductionVariantRoutingConfig {
    /**
     * Sets how the endpoint routes incoming traffic:    LEAST_OUTSTANDING_REQUESTS: The endpoint routes requests to the specific instances that have more capacity to process them.    RANDOM: The endpoint routes each request to a randomly chosen instance.  
     */
    RoutingStrategy: RoutingStrategy;
  }
  export type ProductionVariantSSMAccess = boolean;
  export interface ProductionVariantServerlessConfig {
    /**
     * The memory size of your serverless endpoint. Valid values are in 1 GB increments: 1024 MB, 2048 MB, 3072 MB, 4096 MB, 5120 MB, or 6144 MB.
     */
    MemorySizeInMB: ServerlessMemorySizeInMB;
    /**
     * The maximum number of concurrent invocations your serverless endpoint can process.
     */
    MaxConcurrency: ServerlessMaxConcurrency;
    /**
     * The amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.  This field is not supported for serverless endpoint recommendations for Inference Recommender jobs. For more information about creating an Inference Recommender job, see CreateInferenceRecommendationsJobs. 
     */
    ProvisionedConcurrency?: ServerlessProvisionedConcurrency;
  }
  export interface ProductionVariantServerlessUpdateConfig {
    /**
     * The updated maximum number of concurrent invocations your serverless endpoint can process.
     */
    MaxConcurrency?: ServerlessMaxConcurrency;
    /**
     * The updated amount of provisioned concurrency to allocate for the serverless endpoint. Should be less than or equal to MaxConcurrency.
     */
    ProvisionedConcurrency?: ServerlessProvisionedConcurrency;
  }
  export interface ProductionVariantStatus {
    /**
     * The endpoint variant status which describes the current deployment stage status or operational status.    Creating: Creating inference resources for the production variant.    Deleting: Terminating inference resources for the production variant.    Updating: Updating capacity for the production variant.    ActivatingTraffic: Turning on traffic for the production variant.    Baking: Waiting period to monitor the CloudWatch alarms in the automatic rollback configuration.  
     */
    Status: VariantStatus;
    /**
     * A message that describes the status of the production variant.
     */
    StatusMessage?: VariantStatusMessage;
    /**
     * The start time of the current status change.
     */
    StartTime?: Timestamp;
  }
  export type ProductionVariantStatusList = ProductionVariantStatus[];
  export interface ProductionVariantSummary {
    /**
     * The name of the variant.
     */
    VariantName: VariantName;
    /**
     * An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.
     */
    DeployedImages?: DeployedImages;
    /**
     * The weight associated with the variant.
     */
    CurrentWeight?: VariantWeight;
    /**
     * The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request. 
     */
    DesiredWeight?: VariantWeight;
    /**
     * The number of instances associated with the variant.
     */
    CurrentInstanceCount?: TaskCount;
    /**
     * The number of instances requested in the UpdateEndpointWeightsAndCapacities request. 
     */
    DesiredInstanceCount?: TaskCount;
    /**
     * The endpoint variant status which describes the current deployment stage status or operational status.
     */
    VariantStatus?: ProductionVariantStatusList;
    /**
     * The serverless configuration for the endpoint.
     */
    CurrentServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * The serverless configuration requested for the endpoint update.
     */
    DesiredServerlessConfig?: ProductionVariantServerlessConfig;
    /**
     * Settings that control the range in the number of instances that the endpoint provisions as it scales up or down to accommodate traffic. 
     */
    ManagedInstanceScaling?: ProductionVariantManagedInstanceScaling;
    /**
     * Settings that control how the endpoint routes incoming traffic to the instances that the endpoint hosts.
     */
    RoutingConfig?: ProductionVariantRoutingConfig;
  }
  export type ProductionVariantSummaryList = ProductionVariantSummary[];
  export type ProductionVariantVolumeSizeInGB = number;
  export interface ProfilerConfig {
    /**
     * Path to Amazon S3 storage location for system and framework metrics.
     */
    S3OutputPath?: S3Uri;
    /**
     * A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
     */
    ProfilingIntervalInMilliseconds?: ProfilingIntervalInMilliseconds;
    /**
     * Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. 
     */
    ProfilingParameters?: ProfilingParameters;
    /**
     * Configuration to turn off Amazon SageMaker Debugger's system monitoring and profiling functionality. To turn it off, set to True.
     */
    DisableProfiler?: DisableProfiler;
  }
  export interface ProfilerConfigForUpdate {
    /**
     * Path to Amazon S3 storage location for system and framework metrics.
     */
    S3OutputPath?: S3Uri;
    /**
     * A time interval for capturing system metrics in milliseconds. Available values are 100, 200, 500, 1000 (1 second), 5000 (5 seconds), and 60000 (1 minute) milliseconds. The default value is 500 milliseconds.
     */
    ProfilingIntervalInMilliseconds?: ProfilingIntervalInMilliseconds;
    /**
     * Configuration information for capturing framework metrics. Available key strings for different profiling options are DetailedProfilingConfig, PythonProfilingConfig, and DataLoaderProfilingConfig. The following codes are configuration structures for the ProfilingParameters parameter. To learn more about how to configure the ProfilingParameters parameter, see Use the SageMaker and Debugger Configuration API Operations to Create, Update, and Debug Your Training Job. 
     */
    ProfilingParameters?: ProfilingParameters;
    /**
     * To turn off Amazon SageMaker Debugger monitoring and profiling while a training job is in progress, set to True.
     */
    DisableProfiler?: DisableProfiler;
  }
  export interface ProfilerRuleConfiguration {
    /**
     * The name of the rule configuration. It must be unique relative to other rule configuration names.
     */
    RuleConfigurationName: RuleConfigurationName;
    /**
     * Path to local storage location for output of rules. Defaults to /opt/ml/processing/output/rule/. 
     */
    LocalPath?: DirectoryPath;
    /**
     * Path to Amazon S3 storage location for rules.
     */
    S3OutputPath?: S3Uri;
    /**
     * The Amazon Elastic Container Registry Image for the managed rule evaluation.
     */
    RuleEvaluatorImage: AlgorithmImage;
    /**
     * The instance type to deploy a custom rule for profiling a training job.
     */
    InstanceType?: ProcessingInstanceType;
    /**
     * The size, in GB, of the ML storage volume attached to the processing instance.
     */
    VolumeSizeInGB?: OptionalVolumeSizeInGB;
    /**
     * Runtime configuration for rule container.
     */
    RuleParameters?: RuleParameters;
  }
  export type ProfilerRuleConfigurations = ProfilerRuleConfiguration[];
  export interface ProfilerRuleEvaluationStatus {
    /**
     * The name of the rule configuration.
     */
    RuleConfigurationName?: RuleConfigurationName;
    /**
     * The Amazon Resource Name (ARN) of the rule evaluation job.
     */
    RuleEvaluationJobArn?: ProcessingJobArn;
    /**
     * Status of the rule evaluation.
     */
    RuleEvaluationStatus?: RuleEvaluationStatus;
    /**
     * Details from the rule evaluation.
     */
    StatusDetails?: StatusDetails;
    /**
     * Timestamp when the rule evaluation status was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export type ProfilerRuleEvaluationStatuses = ProfilerRuleEvaluationStatus[];
  export type ProfilingIntervalInMilliseconds = number;
  export type ProfilingParameters = {[key: string]: ConfigValue};
  export type ProfilingStatus = "Enabled"|"Disabled"|string;
  export type ProgrammingLang = string;
  export interface Project {
    /**
     * The Amazon Resource Name (ARN) of the project.
     */
    ProjectArn?: ProjectArn;
    /**
     * The name of the project.
     */
    ProjectName?: ProjectEntityName;
    /**
     * The ID of the project.
     */
    ProjectId?: ProjectId;
    /**
     * The description of the project.
     */
    ProjectDescription?: EntityDescription;
    ServiceCatalogProvisioningDetails?: ServiceCatalogProvisioningDetails;
    ServiceCatalogProvisionedProductDetails?: ServiceCatalogProvisionedProductDetails;
    /**
     * The status of the project.
     */
    ProjectStatus?: ProjectStatus;
    /**
     * Who created the project.
     */
    CreatedBy?: UserContext;
    /**
     * A timestamp specifying when the project was created.
     */
    CreationTime?: Timestamp;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
    /**
     * A timestamp container for when the project was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
  }
  export type ProjectArn = string;
  export type ProjectEntityName = string;
  export type ProjectId = string;
  export type ProjectSortBy = "Name"|"CreationTime"|string;
  export type ProjectSortOrder = "Ascending"|"Descending"|string;
  export type ProjectStatus = "Pending"|"CreateInProgress"|"CreateCompleted"|"CreateFailed"|"DeleteInProgress"|"DeleteFailed"|"DeleteCompleted"|"UpdateInProgress"|"UpdateCompleted"|"UpdateFailed"|string;
  export interface ProjectSummary {
    /**
     * The name of the project.
     */
    ProjectName: ProjectEntityName;
    /**
     * The description of the project.
     */
    ProjectDescription?: EntityDescription;
    /**
     * The Amazon Resource Name (ARN) of the project.
     */
    ProjectArn: ProjectArn;
    /**
     * The ID of the project.
     */
    ProjectId: ProjectId;
    /**
     * The time that the project was created.
     */
    CreationTime: Timestamp;
    /**
     * The status of the project.
     */
    ProjectStatus: ProjectStatus;
  }
  export type ProjectSummaryList = ProjectSummary[];
  export type PropertyNameHint = string;
  export interface PropertyNameQuery {
    /**
     * Text that begins a property's name.
     */
    PropertyNameHint: PropertyNameHint;
  }
  export interface PropertyNameSuggestion {
    /**
     * A suggested property name based on what you entered in the search textbox in the SageMaker console.
     */
    PropertyName?: ResourcePropertyName;
  }
  export type PropertyNameSuggestionList = PropertyNameSuggestion[];
  export type ProvisionedProductStatusMessage = string;
  export interface ProvisioningParameter {
    /**
     * The key that identifies a provisioning parameter.
     */
    Key?: ProvisioningParameterKey;
    /**
     * The value of the provisioning parameter.
     */
    Value?: ProvisioningParameterValue;
  }
  export type ProvisioningParameterKey = string;
  export type ProvisioningParameterValue = string;
  export type ProvisioningParameters = ProvisioningParameter[];
  export interface PublicWorkforceTaskPrice {
    /**
     * Defines the amount of money paid to an Amazon Mechanical Turk worker in United States dollars.
     */
    AmountInUsd?: USD;
  }
  export interface PutModelPackageGroupPolicyInput {
    /**
     * The name of the model group to add a resource policy to.
     */
    ModelPackageGroupName: EntityName;
    /**
     * The resource policy for the model group.
     */
    ResourcePolicy: PolicyString;
  }
  export interface PutModelPackageGroupPolicyOutput {
    /**
     * The Amazon Resource Name (ARN) of the model package group.
     */
    ModelPackageGroupArn: ModelPackageGroupArn;
  }
  export type QProfileArn = string;
  export interface QualityCheckStepMetadata {
    /**
     * The type of the Quality check step.
     */
    CheckType?: String256;
    /**
     * The Amazon S3 URI of the baseline statistics file used for the drift check.
     */
    BaselineUsedForDriftCheckStatistics?: String1024;
    /**
     * The Amazon S3 URI of the baseline constraints file used for the drift check.
     */
    BaselineUsedForDriftCheckConstraints?: String1024;
    /**
     * The Amazon S3 URI of the newly calculated baseline statistics file.
     */
    CalculatedBaselineStatistics?: String1024;
    /**
     * The Amazon S3 URI of the newly calculated baseline constraints file.
     */
    CalculatedBaselineConstraints?: String1024;
    /**
     * The model package group name.
     */
    ModelPackageGroupName?: String256;
    /**
     * The Amazon S3 URI of violation report if violations are detected.
     */
    ViolationReport?: String1024;
    /**
     * The Amazon Resource Name (ARN) of the Quality check processing job that was run by this step execution.
     */
    CheckJobArn?: String256;
    /**
     * This flag indicates if the drift check against the previous baseline will be skipped or not. If it is set to False, the previous baseline of the configured check type must be available.
     */
    SkipCheck?: Boolean;
    /**
     * This flag indicates if a newly calculated baseline can be accessed through step properties BaselineUsedForDriftCheckConstraints and BaselineUsedForDriftCheckStatistics. If it is set to False, the previous baseline of the configured check type must also be available. These can be accessed through the BaselineUsedForDriftCheckConstraints and  BaselineUsedForDriftCheckStatistics properties. 
     */
    RegisterNewBaseline?: Boolean;
  }
  export interface QueryFilters {
    /**
     * Filter the lineage entities connected to the StartArn by type. For example: DataSet, Model, Endpoint, or ModelDeployment.
     */
    Types?: QueryTypes;
    /**
     * Filter the lineage entities connected to the StartArn(s) by the type of the lineage entity.
     */
    LineageTypes?: QueryLineageTypes;
    /**
     * Filter the lineage entities connected to the StartArn(s) by created date.
     */
    CreatedBefore?: Timestamp;
    /**
     * Filter the lineage entities connected to the StartArn(s) after the create date.
     */
    CreatedAfter?: Timestamp;
    /**
     * Filter the lineage entities connected to the StartArn(s) before the last modified date.
     */
    ModifiedBefore?: Timestamp;
    /**
     * Filter the lineage entities connected to the StartArn(s) after the last modified date.
     */
    ModifiedAfter?: Timestamp;
    /**
     * Filter the lineage entities connected to the StartArn(s) by a set if property key value pairs. If multiple pairs are provided, an entity is included in the results if it matches any of the provided pairs.
     */
    Properties?: QueryProperties;
  }
  export type QueryLineageMaxDepth = number;
  export type QueryLineageMaxResults = number;
  export interface QueryLineageRequest {
    /**
     * A list of resource Amazon Resource Name (ARN) that represent the starting point for your lineage query.
     */
    StartArns?: QueryLineageStartArns;
    /**
     * Associations between lineage entities have a direction. This parameter determines the direction from the StartArn(s) that the query traverses.
     */
    Direction?: Direction;
    /**
     *  Setting this value to True retrieves not only the entities of interest but also the Associations and lineage entities on the path. Set to False to only return lineage entities that match your query.
     */
    IncludeEdges?: Boolean;
    /**
     * A set of filtering parameters that allow you to specify which entities should be returned.   Properties - Key-value pairs to match on the lineage entities' properties.   LineageTypes - A set of lineage entity types to match on. For example: TrialComponent, Artifact, or Context.   CreatedBefore - Filter entities created before this date.   ModifiedBefore - Filter entities modified before this date.   ModifiedAfter - Filter entities modified after this date.  
     */
    Filters?: QueryFilters;
    /**
     * The maximum depth in lineage relationships from the StartArns that are traversed. Depth is a measure of the number of Associations from the StartArn entity to the matched results.
     */
    MaxDepth?: QueryLineageMaxDepth;
    /**
     * Limits the number of vertices in the results. Use the NextToken in a response to to retrieve the next page of results.
     */
    MaxResults?: QueryLineageMaxResults;
    /**
     * Limits the number of vertices in the request. Use the NextToken in a response to to retrieve the next page of results.
     */
    NextToken?: String8192;
  }
  export interface QueryLineageResponse {
    /**
     * A list of vertices connected to the start entity(ies) in the lineage graph.
     */
    Vertices?: Vertices;
    /**
     * A list of edges that connect vertices in the response.
     */
    Edges?: Edges;
    /**
     * Limits the number of vertices in the response. Use the NextToken in a response to to retrieve the next page of results.
     */
    NextToken?: String8192;
  }
  export type QueryLineageStartArns = AssociationEntityArn[];
  export type QueryLineageTypes = LineageType[];
  export type QueryProperties = {[key: string]: String256};
  export type QueryTypes = String40[];
  export interface RSessionAppSettings {
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A list of custom SageMaker images that are configured to run as a RSession app.
     */
    CustomImages?: CustomImages;
  }
  export type RStudioServerProAccessStatus = "ENABLED"|"DISABLED"|string;
  export interface RStudioServerProAppSettings {
    /**
     * Indicates whether the current user has access to the RStudioServerPro app.
     */
    AccessStatus?: RStudioServerProAccessStatus;
    /**
     * The level of permissions that the user has within the RStudioServerPro app. This value defaults to `User`. The `Admin` value allows the user access to the RStudio Administrative Dashboard.
     */
    UserGroup?: RStudioServerProUserGroup;
  }
  export interface RStudioServerProDomainSettings {
    /**
     * The ARN of the execution role for the RStudioServerPro Domain-level app.
     */
    DomainExecutionRoleArn: RoleArn;
    /**
     * A URL pointing to an RStudio Connect server.
     */
    RStudioConnectUrl?: String;
    /**
     * A URL pointing to an RStudio Package Manager server.
     */
    RStudioPackageManagerUrl?: String;
    DefaultResourceSpec?: ResourceSpec;
  }
  export interface RStudioServerProDomainSettingsForUpdate {
    /**
     * The execution role for the RStudioServerPro Domain-level app.
     */
    DomainExecutionRoleArn: RoleArn;
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A URL pointing to an RStudio Connect server.
     */
    RStudioConnectUrl?: String;
    /**
     * A URL pointing to an RStudio Package Manager server.
     */
    RStudioPackageManagerUrl?: String;
  }
  export type RStudioServerProUserGroup = "R_STUDIO_ADMIN"|"R_STUDIO_USER"|string;
  export type RandomSeed = number;
  export interface RealTimeInferenceConfig {
    /**
     * The instance type the model is deployed to.
     */
    InstanceType: InstanceType;
    /**
     * The number of instances of the type specified by InstanceType.
     */
    InstanceCount: TaskCount;
  }
  export interface RealTimeInferenceRecommendation {
    /**
     * The recommendation ID which uniquely identifies each recommendation.
     */
    RecommendationId: String;
    /**
     * The recommended instance type for Real-Time Inference.
     */
    InstanceType: ProductionVariantInstanceType;
    /**
     * The recommended environment variables to set in the model container for Real-Time Inference.
     */
    Environment?: EnvironmentMap;
  }
  export type RealTimeInferenceRecommendations = RealTimeInferenceRecommendation[];
  export type RealtimeInferenceInstanceTypes = ProductionVariantInstanceType[];
  export type RecommendationFailureReason = string;
  export type RecommendationJobArn = string;
  export type RecommendationJobCompilationJobName = string;
  export interface RecommendationJobCompiledOutputConfig {
    /**
     * Identifies the Amazon S3 bucket where you want SageMaker to store the compiled model artifacts.
     */
    S3OutputUri?: S3Uri;
  }
  export interface RecommendationJobContainerConfig {
    /**
     * The machine learning domain of the model and its components. Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING 
     */
    Domain?: String;
    /**
     * The machine learning task that the model accomplishes. Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER 
     */
    Task?: String;
    /**
     * The machine learning framework of the container image. Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN 
     */
    Framework?: String;
    /**
     * The framework version of the container image.
     */
    FrameworkVersion?: RecommendationJobFrameworkVersion;
    /**
     * Specifies the SamplePayloadUrl and all other sample payload-related fields.
     */
    PayloadConfig?: RecommendationJobPayloadConfig;
    /**
     * The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model. Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet 
     */
    NearestModelName?: String;
    /**
     * A list of the instance types that are used to generate inferences in real-time.
     */
    SupportedInstanceTypes?: RecommendationJobSupportedInstanceTypes;
    /**
     * The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.
     */
    SupportedEndpointType?: RecommendationJobSupportedEndpointType;
    /**
     * Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.
     */
    DataInputConfig?: RecommendationJobDataInputConfig;
    /**
     * The supported MIME types for the output data.
     */
    SupportedResponseMIMETypes?: RecommendationJobSupportedResponseMIMETypes;
  }
  export type RecommendationJobDataInputConfig = string;
  export type RecommendationJobDescription = string;
  export type RecommendationJobFrameworkVersion = string;
  export interface RecommendationJobInferenceBenchmark {
    Metrics?: RecommendationMetrics;
    EndpointMetrics?: InferenceMetrics;
    EndpointConfiguration?: EndpointOutputConfiguration;
    ModelConfiguration: ModelConfiguration;
    /**
     * The reason why a benchmark failed.
     */
    FailureReason?: RecommendationFailureReason;
    /**
     * A timestamp that shows when the benchmark completed.
     */
    InvocationEndTime?: InvocationEndTime;
    /**
     * A timestamp that shows when the benchmark started.
     */
    InvocationStartTime?: InvocationStartTime;
  }
  export interface RecommendationJobInputConfig {
    /**
     * The Amazon Resource Name (ARN) of a versioned model package.
     */
    ModelPackageVersionArn?: ModelPackageArn;
    /**
     * The name of the created model.
     */
    ModelName?: ModelName;
    /**
     * Specifies the maximum duration of the job, in seconds. The maximum value is 18,000 seconds.
     */
    JobDurationInSeconds?: JobDurationInSeconds;
    /**
     * Specifies the traffic pattern of the job.
     */
    TrafficPattern?: TrafficPattern;
    /**
     * Defines the resource limit of the job.
     */
    ResourceLimit?: RecommendationJobResourceLimit;
    /**
     * Specifies the endpoint configuration to use for a job.
     */
    EndpointConfigurations?: EndpointInputConfigurations;
    /**
     * The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint. This key will be passed to SageMaker Hosting for endpoint creation.  The SageMaker execution role must have kms:CreateGrant permission in order to encrypt data on the storage volume of the endpoints created for inference recommendation. The inference recommendation job will fail asynchronously during endpoint configuration creation if the role passed does not have kms:CreateGrant permission. The KmsKeyId can be any of the following formats:   // KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    // Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:&lt;region&gt;:&lt;account&gt;:key/&lt;key-id-12ab-34cd-56ef-1234567890ab&gt;"    // KMS Key Alias  "alias/ExampleAlias"    // Amazon Resource Name (ARN) of a KMS Key Alias  "arn:aws:kms:&lt;region&gt;:&lt;account&gt;:alias/&lt;ExampleAlias&gt;"    For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
     */
    VolumeKmsKeyId?: KmsKeyId;
    /**
     * Specifies mandatory fields for running an Inference Recommender job. The fields specified in ContainerConfig override the corresponding fields in the model package.
     */
    ContainerConfig?: RecommendationJobContainerConfig;
    /**
     * Existing customer endpoints on which to run an Inference Recommender job.
     */
    Endpoints?: Endpoints;
    /**
     * Inference Recommender provisions SageMaker endpoints with access to VPC in the inference recommendation job.
     */
    VpcConfig?: RecommendationJobVpcConfig;
  }
  export type RecommendationJobName = string;
  export interface RecommendationJobOutputConfig {
    /**
     * The Amazon Resource Name (ARN) of a Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt your output artifacts with Amazon S3 server-side encryption. The SageMaker execution role must have kms:GenerateDataKey permission. The KmsKeyId can be any of the following formats:   // KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    // Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:&lt;region&gt;:&lt;account&gt;:key/&lt;key-id-12ab-34cd-56ef-1234567890ab&gt;"    // KMS Key Alias  "alias/ExampleAlias"    // Amazon Resource Name (ARN) of a KMS Key Alias  "arn:aws:kms:&lt;region&gt;:&lt;account&gt;:alias/&lt;ExampleAlias&gt;"    For more information about key identifiers, see Key identifiers (KeyID) in the Amazon Web Services Key Management Service (Amazon Web Services KMS) documentation.
     */
    KmsKeyId?: KmsKeyId;
    /**
     * Provides information about the output configuration for the compiled model.
     */
    CompiledOutputConfig?: RecommendationJobCompiledOutputConfig;
  }
  export interface RecommendationJobPayloadConfig {
    /**
     * The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).
     */
    SamplePayloadUrl?: S3Uri;
    /**
     * The supported MIME types for the input data.
     */
    SupportedContentTypes?: RecommendationJobSupportedContentTypes;
  }
  export interface RecommendationJobResourceLimit {
    /**
     * Defines the maximum number of load tests.
     */
    MaxNumberOfTests?: MaxNumberOfTests;
    /**
     * Defines the maximum number of parallel load tests.
     */
    MaxParallelOfTests?: MaxParallelOfTests;
  }
  export type RecommendationJobStatus = "PENDING"|"IN_PROGRESS"|"COMPLETED"|"FAILED"|"STOPPING"|"STOPPED"|"DELETING"|"DELETED"|string;
  export interface RecommendationJobStoppingConditions {
    /**
     * The maximum number of requests per minute expected for the endpoint.
     */
    MaxInvocations?: Integer;
    /**
     * The interval of time taken by a model to respond as viewed from SageMaker. The interval includes the local communication time taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
     */
    ModelLatencyThresholds?: ModelLatencyThresholds;
    /**
     * Stops a load test when the number of invocations (TPS) peaks and flattens, which means that the instance has reached capacity. The default value is Stop. If you want the load test to continue after invocations have flattened, set the value to Continue.
     */
    FlatInvocations?: FlatInvocations;
  }
  export type RecommendationJobSupportedContentType = string;
  export type RecommendationJobSupportedContentTypes = RecommendationJobSupportedContentType[];
  export type RecommendationJobSupportedEndpointType = "RealTime"|"Serverless"|string;
  export type RecommendationJobSupportedInstanceTypes = String[];
  export type RecommendationJobSupportedResponseMIMEType = string;
  export type RecommendationJobSupportedResponseMIMETypes = RecommendationJobSupportedResponseMIMEType[];
  export type RecommendationJobType = "Default"|"Advanced"|string;
  export interface RecommendationJobVpcConfig {
    /**
     * The VPC security group IDs. IDs have the form of sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
     */
    SecurityGroupIds: RecommendationJobVpcSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC to which you want to connect your model.
     */
    Subnets: RecommendationJobVpcSubnets;
  }
  export type RecommendationJobVpcSecurityGroupId = string;
  export type RecommendationJobVpcSecurityGroupIds = RecommendationJobVpcSecurityGroupId[];
  export type RecommendationJobVpcSubnetId = string;
  export type RecommendationJobVpcSubnets = RecommendationJobVpcSubnetId[];
  export interface RecommendationMetrics {
    /**
     * Defines the cost per hour for the instance. 
     */
    CostPerHour?: Float;
    /**
     * Defines the cost per inference for the instance .
     */
    CostPerInference?: Float;
    /**
     * The expected maximum number of requests per minute for the instance.
     */
    MaxInvocations?: Integer;
    /**
     * The expected model latency at maximum invocation per minute for the instance.
     */
    ModelLatency?: Integer;
    /**
     * The expected CPU utilization at maximum invocations per minute for the instance.  NaN indicates that the value is not available.
     */
    CpuUtilization?: UtilizationMetric;
    /**
     * The expected memory utilization at maximum invocations per minute for the instance.  NaN indicates that the value is not available.
     */
    MemoryUtilization?: UtilizationMetric;
    /**
     * The time it takes to launch new compute resources for a serverless endpoint. The time can vary depending on the model size, how long it takes to download the model, and the start-up time of the container.  NaN indicates that the value is not available.
     */
    ModelSetupTime?: ModelSetupTime;
  }
  export type RecommendationStatus = "IN_PROGRESS"|"COMPLETED"|"FAILED"|"NOT_APPLICABLE"|string;
  export type RecommendationStepType = "BENCHMARK"|string;
  export type RecordWrapper = "None"|"RecordIO"|string;
  export type RedshiftClusterId = string;
  export type RedshiftDatabase = string;
  export interface RedshiftDatasetDefinition {
    ClusterId: RedshiftClusterId;
    Database: RedshiftDatabase;
    DbUser: RedshiftUserName;
    QueryString: RedshiftQueryString;
    /**
     * The IAM role attached to your Redshift cluster that Amazon SageMaker uses to generate datasets.
     */
    ClusterRoleArn: RoleArn;
    /**
     * The location in Amazon S3 where the Redshift query results are stored.
     */
    OutputS3Uri: S3Uri;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt data from a Redshift execution.
     */
    KmsKeyId?: KmsKeyId;
    OutputFormat: RedshiftResultFormat;
    OutputCompression?: RedshiftResultCompressionType;
  }
  export type RedshiftQueryString = string;
  export type RedshiftResultCompressionType = "None"|"GZIP"|"BZIP2"|"ZSTD"|"SNAPPY"|string;
  export type RedshiftResultFormat = "PARQUET"|"CSV"|string;
  export type RedshiftUserName = string;
  export type ReferenceMinVersion = string;
  export interface RegisterDevicesRequest {
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
    /**
     * A list of devices to register with SageMaker Edge Manager.
     */
    Devices: Devices;
    /**
     * The tags associated with devices.
     */
    Tags?: TagList;
  }
  export interface RegisterModelStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    Arn?: String256;
  }
  export type ReleaseNotes = string;
  export interface RemoteDebugConfig {
    /**
     * If set to True, enables remote debugging.
     */
    EnableRemoteDebug?: EnableRemoteDebug;
  }
  export interface RemoteDebugConfigForUpdate {
    /**
     * If set to True, enables remote debugging.
     */
    EnableRemoteDebug?: EnableRemoteDebug;
  }
  export interface RenderUiTemplateRequest {
    /**
     * A Template object containing the worker UI template to render.
     */
    UiTemplate?: UiTemplate;
    /**
     * A RenderableTask object containing a representative task to render.
     */
    Task: RenderableTask;
    /**
     * The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.
     */
    RoleArn: RoleArn;
    /**
     * The HumanTaskUiArn of the worker UI that you want to render. Do not provide a HumanTaskUiArn if you use the UiTemplate parameter. See a list of available Human Ui Amazon Resource Names (ARNs) in UiConfig.
     */
    HumanTaskUiArn?: HumanTaskUiArn;
  }
  export interface RenderUiTemplateResponse {
    /**
     * A Liquid template that renders the HTML for the worker UI.
     */
    RenderedContent: String;
    /**
     * A list of one or more RenderingError objects if any were encountered while rendering the template. If there were no errors, the list is empty.
     */
    Errors: RenderingErrorList;
  }
  export interface RenderableTask {
    /**
     * A JSON object that contains values for the variables defined in the template. It is made available to the template under the substitution variable task.input. For example, if you define a variable task.input.text in your template, you can supply the variable in the JSON object as "text": "sample text".
     */
    Input: TaskInput;
  }
  export interface RenderingError {
    /**
     * A unique identifier for a specific class of errors.
     */
    Code: String;
    /**
     * A human-readable message describing the error.
     */
    Message: String;
  }
  export type RenderingErrorList = RenderingError[];
  export type RepositoryAccessMode = "Platform"|"Vpc"|string;
  export interface RepositoryAuthConfig {
    /**
     * The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function that provides credentials to authenticate to the private Docker registry where your model image is hosted. For information about how to create an Amazon Web Services Lambda function, see Create a Lambda function with the console in the Amazon Web Services Lambda Developer Guide.
     */
    RepositoryCredentialsProviderArn: RepositoryCredentialsProviderArn;
  }
  export type RepositoryCredentialsProviderArn = string;
  export type RepositoryUrl = string;
  export interface ResolvedAttributes {
    AutoMLJobObjective?: AutoMLJobObjective;
    /**
     * The problem type.
     */
    ProblemType?: ProblemType;
    CompletionCriteria?: AutoMLJobCompletionCriteria;
  }
  export type ResourceArn = string;
  export interface ResourceCatalog {
    /**
     *  The Amazon Resource Name (ARN) of the ResourceCatalog. 
     */
    ResourceCatalogArn: ResourceCatalogArn;
    /**
     *  The name of the ResourceCatalog. 
     */
    ResourceCatalogName: ResourceCatalogName;
    /**
     *  A free form description of the ResourceCatalog. 
     */
    Description: ResourceCatalogDescription;
    /**
     *  The time the ResourceCatalog was created. 
     */
    CreationTime: Timestamp;
  }
  export type ResourceCatalogArn = string;
  export type ResourceCatalogDescription = string;
  export type ResourceCatalogList = ResourceCatalog[];
  export type ResourceCatalogName = string;
  export type ResourceCatalogSortBy = "CreationTime"|string;
  export type ResourceCatalogSortOrder = "Ascending"|"Descending"|string;
  export interface ResourceConfig {
    /**
     * The ML compute instance type.   SageMaker Training on Amazon Elastic Compute Cloud (EC2) P4de instances is in preview release starting December 9th, 2022.   Amazon EC2 P4de instances (currently in preview) are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, which accelerate the speed of training ML models that need to be trained on large datasets of high-resolution data. In this preview release, Amazon SageMaker supports ML training jobs on P4de instances (ml.p4de.24xlarge) to reduce model training time. The ml.p4de.24xlarge instances are available in the following Amazon Web Services Regions.    US East (N. Virginia) (us-east-1)   US West (Oregon) (us-west-2)   To request quota limit increase and start using P4de instances, contact the SageMaker Training service team through your account team. 
     */
    InstanceType?: TrainingInstanceType;
    /**
     * The number of ML compute instances to use. For distributed training, provide a value greater than 1. 
     */
    InstanceCount?: TrainingInstanceCount;
    /**
     * The size of the ML storage volume that you want to provision.  ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.  When using an ML instance with NVMe SSD volumes, SageMaker doesn't provision Amazon EBS General Purpose SSD (gp2) storage. Available storage is fixed to the NVMe-type instance's storage capacity. SageMaker configures storage paths for training datasets, checkpoints, model artifacts, and outputs to use the entire capacity of the instance storage. For example, ML instance families with the NVMe-type instance storage include ml.p4d, ml.g4dn, and ml.g5.  When using an ML instance with the EBS-only storage option and without instance storage, you must define the size of EBS volume through VolumeSizeInGB in the ResourceConfig API. For example, ML instance families that use EBS volumes include ml.c5 and ml.p2.  To look up instance types and their instance storage types and volumes, see Amazon EC2 Instance Types. To find the default local paths defined by the SageMaker training platform, see Amazon SageMaker Training Storage Folders for Training Datasets, Checkpoints, Model Artifacts, and Outputs.
     */
    VolumeSizeInGB: VolumeSizeInGB;
    /**
     * The Amazon Web Services KMS key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.  Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.  The VolumeKmsKeyId can be in any of the following formats:   // KMS Key ID  "1234abcd-12ab-34cd-56ef-1234567890ab"    // Amazon Resource Name (ARN) of a KMS Key  "arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab"   
     */
    VolumeKmsKeyId?: KmsKeyId;
    /**
     * The duration of time in seconds to retain configured resources in a warm pool for subsequent training jobs.
     */
    KeepAlivePeriodInSeconds?: KeepAlivePeriodInSeconds;
    /**
     * The configuration of a heterogeneous cluster in JSON format.
     */
    InstanceGroups?: InstanceGroups;
  }
  export interface ResourceConfigForUpdate {
    /**
     * The KeepAlivePeriodInSeconds value specified in the ResourceConfig to update.
     */
    KeepAlivePeriodInSeconds: KeepAlivePeriodInSeconds;
  }
  export type ResourceId = string;
  export interface ResourceLimits {
    /**
     * The maximum number of training jobs that a hyperparameter tuning job can launch.
     */
    MaxNumberOfTrainingJobs?: MaxNumberOfTrainingJobs;
    /**
     * The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.
     */
    MaxParallelTrainingJobs: MaxParallelTrainingJobs;
    /**
     * The maximum time in seconds that a hyperparameter tuning job can run.
     */
    MaxRuntimeInSeconds?: HyperParameterTuningMaxRuntimeInSeconds;
  }
  export type ResourcePolicyString = string;
  export type ResourcePropertyName = string;
  export type ResourceRetainedBillableTimeInSeconds = number;
  export interface ResourceSpec {
    /**
     * The ARN of the SageMaker image that the image version belongs to.
     */
    SageMakerImageArn?: ImageArn;
    /**
     * The ARN of the image version created on the instance.
     */
    SageMakerImageVersionArn?: ImageVersionArn;
    /**
     * The SageMakerImageVersionAlias of the image to launch with. This value is in SemVer 2.0.0 versioning format.
     */
    SageMakerImageVersionAlias?: ImageVersionAlias;
    /**
     * The instance type that the image version runs on.   JupyterServer apps only support the system value. For KernelGateway apps, the system value is translated to ml.t3.medium. KernelGateway apps also support all other values for available instance types. 
     */
    InstanceType?: AppInstanceType;
    /**
     *  The Amazon Resource Name (ARN) of the Lifecycle Configuration attached to the Resource.
     */
    LifecycleConfigArn?: StudioLifecycleConfigArn;
  }
  export type ResourceType = "TrainingJob"|"Experiment"|"ExperimentTrial"|"ExperimentTrialComponent"|"Endpoint"|"Model"|"ModelPackage"|"ModelPackageGroup"|"Pipeline"|"PipelineExecution"|"FeatureGroup"|"FeatureMetadata"|"Image"|"ImageVersion"|"Project"|"HyperParameterTuningJob"|"ModelCard"|string;
  export type ResponseMIMEType = string;
  export type ResponseMIMETypes = ResponseMIMEType[];
  export interface RetentionPolicy {
    /**
     * The default is Retain, which specifies to keep the data stored on the Amazon EFS volume. Specify Delete to delete the data stored on the Amazon EFS volume.
     */
    HomeEfsFileSystem?: RetentionType;
  }
  export type RetentionType = "Retain"|"Delete"|string;
  export interface RetryPipelineExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
     */
    ClientRequestToken: IdempotencyToken;
    /**
     * This configuration, if specified, overrides the parallelism configuration of the parent pipeline.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
  }
  export interface RetryPipelineExecutionResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface RetryStrategy {
    /**
     * The number of times to retry the job. When the job is retried, it's SecondaryStatus is changed to STARTING.
     */
    MaximumRetryAttempts: MaximumRetryAttempts;
  }
  export type RoleArn = string;
  export interface RollingUpdatePolicy {
    /**
     * Batch size for each rolling step to provision capacity and turn on traffic on the new endpoint fleet, and terminate capacity on the old endpoint fleet. Value must be between 5% to 50% of the variant's total instance count.
     */
    MaximumBatchSize: CapacitySize;
    /**
     * The length of the baking period, during which SageMaker monitors alarms for each batch on the new fleet.
     */
    WaitIntervalInSeconds: WaitIntervalInSeconds;
    /**
     * The time limit for the total deployment. Exceeding this limit causes a timeout.
     */
    MaximumExecutionTimeoutInSeconds?: MaximumExecutionTimeoutInSeconds;
    /**
     * Batch size for rollback to the old endpoint fleet. Each rolling step to provision capacity and turn on traffic on the old endpoint fleet, and terminate capacity on the new endpoint fleet. If this field is absent, the default value will be set to 100% of total capacity which means to bring up the whole capacity of the old fleet at once during rollback.
     */
    RollbackMaximumBatchSize?: CapacitySize;
  }
  export type RootAccess = "Enabled"|"Disabled"|string;
  export type RoutingStrategy = "LEAST_OUTSTANDING_REQUESTS"|"RANDOM"|string;
  export type RuleConfigurationName = string;
  export type RuleEvaluationStatus = "InProgress"|"NoIssuesFound"|"IssuesFound"|"Error"|"Stopping"|"Stopped"|string;
  export type RuleParameters = {[key: string]: ConfigValue};
  export type S3DataDistribution = "FullyReplicated"|"ShardedByS3Key"|string;
  export interface S3DataSource {
    /**
     * If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix for model training.  If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want SageMaker to use for model training.  If you choose AugmentedManifestFile, S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. AugmentedManifestFile can only be used if the Channel's input mode is Pipe.
     */
    S3DataType: S3DataType;
    /**
     * Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:     A key name prefix might look like this: s3://bucketname/exampleprefix/     A manifest might look like this: s3://bucketname/example.manifest   A manifest is an S3 object which is a JSON file consisting of an array of elements. The first element is a prefix which is followed by one or more suffixes. SageMaker appends the suffix elements to the prefix to get a full set of S3Uri. Note that the prefix must be a valid non-empty S3Uri that precludes users from specifying a manifest whose individual S3Uri is sourced from different S3 buckets.  The following code example shows a valid manifest format:   [ {"prefix": "s3://customer_bucket/some/prefix/"},    "relative/path/to/custdata-1",    "relative/path/custdata-2",    ...    "relative/path/custdata-N"   ]   This JSON is equivalent to the following S3Uri list:  s3://customer_bucket/some/prefix/relative/path/to/custdata-1   s3://customer_bucket/some/prefix/relative/path/custdata-2   ...   s3://customer_bucket/some/prefix/relative/path/custdata-N  The complete set of S3Uri in this manifest is the input data for the channel for this data source. The object that each S3Uri points to must be readable by the IAM role that SageMaker uses to perform tasks on your behalf.    Your input bucket must be located in same Amazon Web Services region as your training job.
     */
    S3Uri: S3Uri;
    /**
     * If you want SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.  If you want SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.  Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms.  In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects. 
     */
    S3DataDistributionType?: S3DataDistribution;
    /**
     * A list of one or more attribute names to use that are found in a specified augmented manifest file.
     */
    AttributeNames?: AttributeNames;
    /**
     * A list of names of instance groups that get data from the S3 data source.
     */
    InstanceGroupNames?: InstanceGroupNames;
  }
  export type S3DataType = "ManifestFile"|"S3Prefix"|"AugmentedManifestFile"|string;
  export interface S3ModelDataSource {
    /**
     * Specifies the S3 path of ML model data to deploy.
     */
    S3Uri: S3ModelUri;
    /**
     * Specifies the type of ML model data to deploy. If you choose S3Prefix, S3Uri identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by S3Uri always ends with a forward slash (/). If you choose S3Object, S3Uri identifies an object that is the ML model data to deploy.
     */
    S3DataType: S3ModelDataType;
    /**
     * Specifies how the ML model data is prepared. If you choose Gzip and choose S3Object as the value of S3DataType, S3Uri identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment. If you choose None and chooose S3Object as the value of S3DataType, S3Uri identifies an object that represents an uncompressed ML model to deploy. If you choose None and choose S3Prefix as the value of S3DataType, S3Uri identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy. If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:   If you choose S3Object as the value of S3DataType, then SageMaker will split the key of the S3 object referenced by S3Uri by slash (/), and use the last part as the filename of the file holding the content of the S3 object.   If you choose S3Prefix as the value of S3DataType, then for each S3 object under the key name pefix referenced by S3Uri, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to /opt/ml/model) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.   Do not use any of the following as file names or directory names:   An empty or blank string   A string which contains null bytes   A string longer than 255 bytes   A single dot (.)   A double dot (..)     Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects s3://mybucket/model/weights and s3://mybucket/model/weights/part1 and you specify s3://mybucket/model/ as the value of S3Uri and S3Prefix as the value of S3DataType, then it will result in name clash between /opt/ml/model/weights (a regular file) and /opt/ml/model/weights/ (a directory).   Do not organize the model artifacts in S3 console using folders. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.   
     */
    CompressionType: ModelCompressionType;
    /**
     * Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ModelAccessConfig. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.
     */
    ModelAccessConfig?: ModelAccessConfig;
    /**
     * Configuration information for hub access.
     */
    HubAccessConfig?: InferenceHubAccessConfig;
  }
  export type S3ModelDataType = "S3Prefix"|"S3Object"|string;
  export type S3ModelUri = string;
  export type S3OutputPath = string;
  export interface S3Presign {
    /**
     * Use this parameter to specify the allowed request source. Possible sources are either SourceIp or VpcSourceIp.
     */
    IamPolicyConstraints?: IamPolicyConstraints;
  }
  export interface S3StorageConfig {
    /**
     * The S3 URI, or location in Amazon S3, of OfflineStore. S3 URIs have a format similar to the following: s3://example-bucket/prefix/.
     */
    S3Uri: S3Uri;
    /**
     * The Amazon Web Services Key Management Service (KMS) key ARN of the key used to encrypt any objects written into the OfflineStore S3 location. The IAM roleARN that is passed as a parameter to CreateFeatureGroup must have below permissions to the KmsKeyId:    "kms:GenerateDataKey"   
     */
    KmsKeyId?: KmsKeyId;
    /**
     * The S3 path where offline records are written.
     */
    ResolvedOutputS3Uri?: S3Uri;
  }
  export type S3Uri = string;
  export type SageMakerImageVersionAlias = string;
  export type SageMakerImageVersionAliases = SageMakerImageVersionAlias[];
  export type SageMakerPublicHubContentArn = string;
  export type SagemakerServicecatalogStatus = "Enabled"|"Disabled"|string;
  export type SampleWeightAttributeName = string;
  export type SamplingPercentage = number;
  export type ScalingPolicies = ScalingPolicy[];
  export interface ScalingPolicy {
    /**
     * A target tracking scaling policy. Includes support for predefined or customized metrics.
     */
    TargetTracking?: TargetTrackingScalingPolicyConfiguration;
  }
  export interface ScalingPolicyMetric {
    /**
     * The number of invocations sent to a model, normalized by InstanceCount in each ProductionVariant. 1/numberOfInstances is sent as the value on each request, where numberOfInstances is the number of active instances for the ProductionVariant behind the endpoint at the time of the request.
     */
    InvocationsPerInstance?: Integer;
    /**
     * The interval of time taken by a model to respond as viewed from SageMaker. This interval includes the local communication times taken to send the request and to fetch the response from the container of a model and the time taken to complete the inference in the container.
     */
    ModelLatency?: Integer;
  }
  export interface ScalingPolicyObjective {
    /**
     * The minimum number of expected requests to your endpoint per minute.
     */
    MinInvocationsPerMinute?: Integer;
    /**
     * The maximum number of expected requests to your endpoint per minute.
     */
    MaxInvocationsPerMinute?: Integer;
  }
  export interface ScheduleConfig {
    /**
     * A cron expression that describes details about the monitoring schedule. The supported cron expressions are:   If you want to set the job to start every hour, use the following:  Hourly: cron(0 * ? * * *)    If you want to start the job daily:  cron(0 [00-23] ? * * *)    If you want to run the job one time, immediately, use the following keyword:  NOW    For example, the following are valid cron expressions:   Daily at noon UTC: cron(0 12 ? * * *)    Daily at midnight UTC: cron(0 0 ? * * *)    To support running every 6, 12 hours, the following are also supported:  cron(0 [00-23]/[01-24] ? * * *)  For example, the following are valid cron expressions:   Every 12 hours, starting at 5pm UTC: cron(0 17/12 ? * * *)    Every two hours starting at midnight: cron(0 0/2 ? * * *)       Even though the cron expression is set to start at 5PM UTC, note that there could be a delay of 0-20 minutes from the actual requested time to run the execution.    We recommend that if you would like a daily schedule, you do not provide this parameter. Amazon SageMaker will pick a time for running every day.    You can also specify the keyword NOW to run the monitoring job immediately, one time, without recurring.
     */
    ScheduleExpression: ScheduleExpression;
    /**
     * Sets the start time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to monitor the five hours of data in your dataset that precede the start of each monitoring job, you would specify: "-PT5H". The start time that you specify must not precede the end time that you specify by more than 24 hours. You specify the end time with the DataAnalysisEndTime parameter. If you set ScheduleExpression to NOW, this parameter is required.
     */
    DataAnalysisStartTime?: String;
    /**
     * Sets the end time for a monitoring job window. Express this time as an offset to the times that you schedule your monitoring jobs to run. You schedule monitoring jobs with the ScheduleExpression parameter. Specify this offset in ISO 8601 duration format. For example, if you want to end the window one hour before the start of each monitoring job, you would specify: "-PT1H". The end time that you specify must not follow the start time that you specify by more than 24 hours. You specify the start time with the DataAnalysisStartTime parameter. If you set ScheduleExpression to NOW, this parameter is required.
     */
    DataAnalysisEndTime?: String;
  }
  export type ScheduleExpression = string;
  export type ScheduleStatus = "Pending"|"Failed"|"Scheduled"|"Stopped"|string;
  export type Scope = string;
  export interface SearchExpression {
    /**
     * A list of filter objects.
     */
    Filters?: FilterList;
    /**
     * A list of nested filter objects.
     */
    NestedFilters?: NestedFiltersList;
    /**
     * A list of search expression objects.
     */
    SubExpressions?: SearchExpressionList;
    /**
     * A Boolean operator used to evaluate the search expression. If you want every conditional statement in all lists to be satisfied for the entire search expression to be true, specify And. If only a single conditional statement needs to be true for the entire search expression to be true, specify Or. The default value is And.
     */
    Operator?: BooleanOperator;
  }
  export type SearchExpressionList = SearchExpression[];
  export interface SearchRecord {
    /**
     * The properties of a training job.
     */
    TrainingJob?: TrainingJob;
    /**
     * The properties of an experiment.
     */
    Experiment?: Experiment;
    /**
     * The properties of a trial.
     */
    Trial?: Trial;
    /**
     * The properties of a trial component.
     */
    TrialComponent?: TrialComponent;
    Endpoint?: Endpoint;
    ModelPackage?: ModelPackage;
    ModelPackageGroup?: ModelPackageGroup;
    Pipeline?: Pipeline;
    PipelineExecution?: PipelineExecution;
    FeatureGroup?: FeatureGroup;
    /**
     * The feature metadata used to search through the features.
     */
    FeatureMetadata?: FeatureMetadata;
    /**
     * The properties of a project.
     */
    Project?: Project;
    /**
     * The properties of a hyperparameter tuning job.
     */
    HyperParameterTuningJob?: HyperParameterTuningJobSearchEntity;
    /**
     * An Amazon SageMaker Model Card that documents details about a machine learning model.
     */
    ModelCard?: ModelCard;
    Model?: ModelDashboardModel;
  }
  export interface SearchRequest {
    /**
     * The name of the SageMaker resource to search for.
     */
    Resource: ResourceType;
    /**
     * A Boolean conditional statement. Resources must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.
     */
    SearchExpression?: SearchExpression;
    /**
     * The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.
     */
    SortBy?: ResourcePropertyName;
    /**
     * How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.
     */
    SortOrder?: SearchSortOrder;
    /**
     * If more than MaxResults resources match the specified SearchExpression, the response includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results.
     */
    NextToken?: NextToken;
    /**
     * The maximum number of results to return.
     */
    MaxResults?: MaxResults;
    /**
     *  A cross account filter option. When the value is "CrossAccount" the search results will only include resources made discoverable to you from other accounts. When the value is "SameAccount" or null the search results will only include resources from your account. Default is null. For more information on searching for resources made discoverable to your account, see  Search discoverable resources in the SageMaker Developer Guide. The maximum number of ResourceCatalogs viewable is 1000. 
     */
    CrossAccountFilterOption?: CrossAccountFilterOption;
    /**
     *  Limits the results of your search request to the resources that you can access. 
     */
    VisibilityConditions?: VisibilityConditionsList;
  }
  export interface SearchResponse {
    /**
     * A list of SearchRecord objects.
     */
    Results?: SearchResultsList;
    /**
     * If the result of the previous Search request was truncated, the response includes a NextToken. To retrieve the next set of results, use the token in the next request.
     */
    NextToken?: NextToken;
  }
  export type SearchResultsList = SearchRecord[];
  export type SearchSortOrder = "Ascending"|"Descending"|string;
  export type SecondaryStatus = "Starting"|"LaunchingMLInstances"|"PreparingTrainingStack"|"Downloading"|"DownloadingTrainingImage"|"Training"|"Uploading"|"Stopping"|"Stopped"|"MaxRuntimeExceeded"|"Completed"|"Failed"|"Interrupted"|"MaxWaitTimeExceeded"|"Updating"|"Restarting"|"Pending"|string;
  export interface SecondaryStatusTransition {
    /**
     * Contains a secondary status information from a training job. Status might be one of the following secondary statuses:  InProgress     Starting - Starting the training job.    Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.    Training - Training is in progress.    Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.    Completed     Completed - The training job has completed.    Failed     Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.    Stopped     MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.    Stopped - The training job has stopped.    Stopping     Stopping - Stopping the training job.     We no longer support the following secondary statuses:    LaunchingMLInstances     PreparingTrainingStack     DownloadingTrainingImage   
     */
    Status: SecondaryStatus;
    /**
     * A timestamp that shows when the training job transitioned to the current secondary status state.
     */
    StartTime: Timestamp;
    /**
     * A timestamp that shows when the training job transitioned out of this secondary status state into another secondary status state or when the training job has ended.
     */
    EndTime?: Timestamp;
    /**
     * A detailed description of the progress within a secondary status.  SageMaker provides secondary statuses and status messages that apply to each of them:  Starting    Starting the training job.   Launching requested ML instances.   Insufficient capacity error from EC2 while launching instances, retrying!   Launched instance was unhealthy, replacing it!   Preparing the instances for training.    Training    Training image download completed. Training in progress.      Status messages are subject to change. Therefore, we recommend not including them in code that programmatically initiates actions. For examples, don't use status messages in if statements.  To have an overview of your training job's progress, view TrainingJobStatus and SecondaryStatus in DescribeTrainingJob, and StatusMessage together. For example, at the start of a training job, you might see the following:    TrainingJobStatus - InProgress    SecondaryStatus - Training    StatusMessage - Downloading the training image  
     */
    StatusMessage?: StatusMessage;
  }
  export type SecondaryStatusTransitions = SecondaryStatusTransition[];
  export type SecretArn = string;
  export type SecurityGroupId = string;
  export type SecurityGroupIds = SecurityGroupId[];
  export type Seed = number;
  export interface SelectedStep {
    /**
     * The name of the pipeline step.
     */
    StepName: String256;
  }
  export type SelectedStepList = SelectedStep[];
  export interface SelectiveExecutionConfig {
    /**
     * The ARN from a reference execution of the current pipeline. Used to copy input collaterals needed for the selected steps to run. The execution status of the pipeline can be either Failed or Success. This field is required if the steps you specify for SelectedSteps depend on output collaterals from any non-specified pipeline steps. For more information, see Selective Execution for Pipeline Steps.
     */
    SourcePipelineExecutionArn?: PipelineExecutionArn;
    /**
     * A list of pipeline steps to run. All step(s) in all path(s) between two selected steps should be included.
     */
    SelectedSteps: SelectedStepList;
  }
  export interface SelectiveExecutionResult {
    /**
     * The ARN from an execution of the current pipeline.
     */
    SourcePipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface SendPipelineExecutionStepFailureRequest {
    /**
     * The pipeline generated token from the Amazon SQS queue.
     */
    CallbackToken: CallbackToken;
    /**
     * A message describing why the step failed.
     */
    FailureReason?: String256;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
     */
    ClientRequestToken?: IdempotencyToken;
  }
  export interface SendPipelineExecutionStepFailureResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface SendPipelineExecutionStepSuccessRequest {
    /**
     * The pipeline generated token from the Amazon SQS queue.
     */
    CallbackToken: CallbackToken;
    /**
     * A list of the output parameters of the callback step.
     */
    OutputParameters?: OutputParameterList;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than one time.
     */
    ClientRequestToken?: IdempotencyToken;
  }
  export interface SendPipelineExecutionStepSuccessResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export type ServerlessMaxConcurrency = number;
  export type ServerlessMemorySizeInMB = number;
  export type ServerlessProvisionedConcurrency = number;
  export type ServiceCatalogEntityId = string;
  export interface ServiceCatalogProvisionedProductDetails {
    /**
     * The ID of the provisioned product.
     */
    ProvisionedProductId?: ServiceCatalogEntityId;
    /**
     * The current status of the product.    AVAILABLE - Stable state, ready to perform any operation. The most recent operation succeeded and completed.    UNDER_CHANGE - Transitive state. Operations performed might not have valid results. Wait for an AVAILABLE status before performing operations.    TAINTED - Stable state, ready to perform any operation. The stack has completed the requested operation but is not exactly what was requested. For example, a request to update to a new version failed and the stack rolled back to the current version.    ERROR - An unexpected error occurred. The provisioned product exists but the stack is not running. For example, CloudFormation received a parameter value that was not valid and could not launch the stack.    PLAN_IN_PROGRESS - Transitive state. The plan operations were performed to provision a new product, but resources have not yet been created. After reviewing the list of resources to be created, execute the plan. Wait for an AVAILABLE status before performing operations.  
     */
    ProvisionedProductStatusMessage?: ProvisionedProductStatusMessage;
  }
  export interface ServiceCatalogProvisioningDetails {
    /**
     * The ID of the product to provision.
     */
    ProductId: ServiceCatalogEntityId;
    /**
     * The ID of the provisioning artifact.
     */
    ProvisioningArtifactId?: ServiceCatalogEntityId;
    /**
     * The path identifier of the product. This value is optional if the product has a default path, and required if the product has more than one path. 
     */
    PathId?: ServiceCatalogEntityId;
    /**
     * A list of key value pairs that you specify when you provision a product.
     */
    ProvisioningParameters?: ProvisioningParameters;
  }
  export interface ServiceCatalogProvisioningUpdateDetails {
    /**
     * The ID of the provisioning artifact.
     */
    ProvisioningArtifactId?: ServiceCatalogEntityId;
    /**
     * A list of key value pairs that you specify when you provision a product.
     */
    ProvisioningParameters?: ProvisioningParameters;
  }
  export interface SessionChainingConfig {
    /**
     * Set to True to allow SageMaker to extract session tags from a training job creation role and reuse these tags when assuming the training job execution role.
     */
    EnableSessionTagChaining?: EnableSessionTagChaining;
  }
  export type SessionExpirationDurationInSeconds = number;
  export interface ShadowModeConfig {
    /**
     *  The name of the production variant, which takes all the inference requests. 
     */
    SourceModelVariantName: ModelVariantName;
    /**
     * List of shadow variant configurations.
     */
    ShadowModelVariants: ShadowModelVariantConfigList;
  }
  export interface ShadowModelVariantConfig {
    /**
     * The name of the shadow variant.
     */
    ShadowModelVariantName: ModelVariantName;
    /**
     *  The percentage of inference requests that Amazon SageMaker replicates from the production variant to the shadow variant. 
     */
    SamplingPercentage: Percentage;
  }
  export type ShadowModelVariantConfigList = ShadowModelVariantConfig[];
  export interface SharingSettings {
    /**
     * Whether to include the notebook cell output when sharing the notebook. The default is Disabled.
     */
    NotebookOutputOption?: NotebookOutputOption;
    /**
     * When NotebookOutputOption is Allowed, the Amazon S3 bucket used to store the shared notebook snapshots.
     */
    S3OutputPath?: S3Uri;
    /**
     * When NotebookOutputOption is Allowed, the Amazon Web Services Key Management Service (KMS) encryption key ID used to encrypt the notebook cell output in the Amazon S3 bucket.
     */
    S3KmsKeyId?: KmsKeyId;
  }
  export type SharingType = "Private"|"Shared"|string;
  export interface ShuffleConfig {
    /**
     * Determines the shuffling order in ShuffleConfig value.
     */
    Seed: Seed;
  }
  export type SingleSignOnApplicationArn = string;
  export type SingleSignOnUserIdentifier = string;
  export type SkipModelValidation = "All"|"None"|string;
  export type SnsTopicArn = string;
  export type SortActionsBy = "Name"|"CreationTime"|string;
  export type SortArtifactsBy = "CreationTime"|string;
  export type SortAssociationsBy = "SourceArn"|"DestinationArn"|"SourceType"|"DestinationType"|"CreationTime"|string;
  export type SortBy = "Name"|"CreationTime"|"Status"|string;
  export type SortContextsBy = "Name"|"CreationTime"|string;
  export type SortExperimentsBy = "Name"|"CreationTime"|string;
  export type SortInferenceExperimentsBy = "Name"|"CreationTime"|"Status"|string;
  export type SortLineageGroupsBy = "Name"|"CreationTime"|string;
  export type SortOrder = "Ascending"|"Descending"|string;
  export type SortPipelineExecutionsBy = "CreationTime"|"PipelineExecutionArn"|string;
  export type SortPipelinesBy = "Name"|"CreationTime"|string;
  export type SortTrackingServerBy = "Name"|"CreationTime"|"Status"|string;
  export type SortTrialComponentsBy = "Name"|"CreationTime"|string;
  export type SortTrialsBy = "Name"|"CreationTime"|string;
  export interface SourceAlgorithm {
    /**
     * The Amazon S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).  The model artifacts must be in an S3 bucket that is in the same Amazon Web Services region as the algorithm. 
     */
    ModelDataUrl?: Url;
    /**
     * Specifies the location of ML model data to deploy during endpoint creation.
     */
    ModelDataSource?: ModelDataSource;
    /**
     * The name of an algorithm that was used to create the model package. The algorithm must be either an algorithm resource in your SageMaker account or an algorithm in Amazon Web Services Marketplace that you are subscribed to.
     */
    AlgorithmName: ArnOrName;
  }
  export type SourceAlgorithmList = SourceAlgorithm[];
  export interface SourceAlgorithmSpecification {
    /**
     * A list of the algorithms that were used to create a model package.
     */
    SourceAlgorithms: SourceAlgorithmList;
  }
  export interface SourceIpConfig {
    /**
     * A list of one to ten Classless Inter-Domain Routing (CIDR) values. Maximum: Ten CIDR values  The following Length Constraints apply to individual CIDR values in the CIDR value list. 
     */
    Cidrs: Cidrs;
  }
  export type SourceType = string;
  export type SourceUri = string;
  export interface SpaceAppLifecycleManagement {
    /**
     * Settings related to idle shutdown of Studio applications.
     */
    IdleSettings?: SpaceIdleSettings;
  }
  export type SpaceArn = string;
  export interface SpaceCodeEditorAppSettings {
    DefaultResourceSpec?: ResourceSpec;
    /**
     * Settings that are used to configure and manage the lifecycle of CodeEditor applications in a space.
     */
    AppLifecycleManagement?: SpaceAppLifecycleManagement;
  }
  export interface SpaceDetails {
    /**
     * The ID of the associated domain.
     */
    DomainId?: DomainId;
    /**
     * The name of the space.
     */
    SpaceName?: SpaceName;
    /**
     * The status.
     */
    Status?: SpaceStatus;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
    /**
     * Specifies summary information about the space settings.
     */
    SpaceSettingsSummary?: SpaceSettingsSummary;
    /**
     * Specifies summary information about the space sharing settings.
     */
    SpaceSharingSettingsSummary?: SpaceSharingSettingsSummary;
    /**
     * Specifies summary information about the ownership settings.
     */
    OwnershipSettingsSummary?: OwnershipSettingsSummary;
    /**
     * The name of the space that appears in the Studio UI.
     */
    SpaceDisplayName?: NonEmptyString64;
  }
  export type SpaceEbsVolumeSizeInGb = number;
  export interface SpaceIdleSettings {
    /**
     * The time that SageMaker waits after the application becomes idle before shutting it down.
     */
    IdleTimeoutInMinutes?: IdleTimeoutInMinutes;
  }
  export interface SpaceJupyterLabAppSettings {
    DefaultResourceSpec?: ResourceSpec;
    /**
     * A list of Git repositories that SageMaker automatically displays to users for cloning in the JupyterLab application.
     */
    CodeRepositories?: CodeRepositories;
    /**
     * Settings that are used to configure and manage the lifecycle of JupyterLab applications in a space.
     */
    AppLifecycleManagement?: SpaceAppLifecycleManagement;
  }
  export type SpaceList = SpaceDetails[];
  export type SpaceName = string;
  export interface SpaceSettings {
    JupyterServerAppSettings?: JupyterServerAppSettings;
    KernelGatewayAppSettings?: KernelGatewayAppSettings;
    /**
     * The Code Editor application settings.
     */
    CodeEditorAppSettings?: SpaceCodeEditorAppSettings;
    /**
     * The settings for the JupyterLab application.
     */
    JupyterLabAppSettings?: SpaceJupyterLabAppSettings;
    /**
     * The type of app created within the space.
     */
    AppType?: AppType;
    /**
     * The storage settings for a space.
     */
    SpaceStorageSettings?: SpaceStorageSettings;
    /**
     * A file system, created by you, that you assign to a space for an Amazon SageMaker Domain. Permitted users can access this file system in Amazon SageMaker Studio.
     */
    CustomFileSystems?: CustomFileSystems;
  }
  export interface SpaceSettingsSummary {
    /**
     * The type of app created within the space.
     */
    AppType?: AppType;
    /**
     * The storage settings for a space.
     */
    SpaceStorageSettings?: SpaceStorageSettings;
  }
  export interface SpaceSharingSettings {
    /**
     * Specifies the sharing type of the space.
     */
    SharingType: SharingType;
  }
  export interface SpaceSharingSettingsSummary {
    /**
     * Specifies the sharing type of the space.
     */
    SharingType?: SharingType;
  }
  export type SpaceSortKey = "CreationTime"|"LastModifiedTime"|string;
  export type SpaceStatus = "Deleting"|"Failed"|"InService"|"Pending"|"Updating"|"Update_Failed"|"Delete_Failed"|string;
  export interface SpaceStorageSettings {
    /**
     * A collection of EBS storage settings for a space.
     */
    EbsStorageSettings?: EbsStorageSettings;
  }
  export type SpawnRate = number;
  export type SplitType = "None"|"Line"|"RecordIO"|"TFRecord"|string;
  export type StageStatus = "CREATING"|"READYTODEPLOY"|"STARTING"|"INPROGRESS"|"DEPLOYED"|"FAILED"|"STOPPING"|"STOPPED"|string;
  export interface Stairs {
    /**
     * Defines how long each traffic step should be.
     */
    DurationInSeconds?: TrafficDurationInSeconds;
    /**
     * Specifies how many steps to perform during traffic.
     */
    NumberOfSteps?: NumberOfSteps;
    /**
     * Specifies how many new users to spawn in each step.
     */
    UsersPerStep?: UsersPerStep;
  }
  export interface StartEdgeDeploymentStageRequest {
    /**
     * The name of the edge deployment plan to start.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * The name of the stage to start.
     */
    StageName: EntityName;
  }
  export interface StartInferenceExperimentRequest {
    /**
     * The name of the inference experiment to start.
     */
    Name: InferenceExperimentName;
  }
  export interface StartInferenceExperimentResponse {
    /**
     * The ARN of the started inference experiment to start.
     */
    InferenceExperimentArn: InferenceExperimentArn;
  }
  export interface StartMlflowTrackingServerRequest {
    /**
     * The name of the tracking server to start.
     */
    TrackingServerName: TrackingServerName;
  }
  export interface StartMlflowTrackingServerResponse {
    /**
     * The ARN of the started tracking server.
     */
    TrackingServerArn?: TrackingServerArn;
  }
  export interface StartMonitoringScheduleRequest {
    /**
     * The name of the schedule to start.
     */
    MonitoringScheduleName: MonitoringScheduleName;
  }
  export interface StartNotebookInstanceInput {
    /**
     * The name of the notebook instance to start.
     */
    NotebookInstanceName: NotebookInstanceName;
  }
  export interface StartPipelineExecutionRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the pipeline.
     */
    PipelineName: PipelineNameOrArn;
    /**
     * The display name of the pipeline execution.
     */
    PipelineExecutionDisplayName?: PipelineExecutionName;
    /**
     * Contains a list of pipeline parameters. This list can be empty. 
     */
    PipelineParameters?: ParameterList;
    /**
     * The description of the pipeline execution.
     */
    PipelineExecutionDescription?: PipelineExecutionDescription;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
     */
    ClientRequestToken: IdempotencyToken;
    /**
     * This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
    /**
     * The selective execution configuration applied to the pipeline run.
     */
    SelectiveExecutionConfig?: SelectiveExecutionConfig;
  }
  export interface StartPipelineExecutionResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export type Statistic = "Average"|"Minimum"|"Maximum"|"SampleCount"|"Sum"|string;
  export type StatusDetails = string;
  export type StatusMessage = string;
  export type StepDescription = string;
  export type StepDisplayName = string;
  export type StepName = string;
  export type StepStatus = "Starting"|"Executing"|"Stopping"|"Stopped"|"Failed"|"Succeeded"|string;
  export interface StopAutoMLJobRequest {
    /**
     * The name of the object you are requesting.
     */
    AutoMLJobName: AutoMLJobName;
  }
  export interface StopCompilationJobRequest {
    /**
     * The name of the model compilation job to stop.
     */
    CompilationJobName: EntityName;
  }
  export interface StopEdgeDeploymentStageRequest {
    /**
     * The name of the edge deployment plan to stop.
     */
    EdgeDeploymentPlanName: EntityName;
    /**
     * The name of the stage to stop.
     */
    StageName: EntityName;
  }
  export interface StopEdgePackagingJobRequest {
    /**
     * The name of the edge packaging job.
     */
    EdgePackagingJobName: EntityName;
  }
  export interface StopHyperParameterTuningJobRequest {
    /**
     * The name of the tuning job to stop.
     */
    HyperParameterTuningJobName: HyperParameterTuningJobName;
  }
  export interface StopInferenceExperimentRequest {
    /**
     * The name of the inference experiment to stop.
     */
    Name: InferenceExperimentName;
    /**
     *  Array of key-value pairs, with names of variants mapped to actions. The possible actions are the following:     Promote - Promote the shadow variant to a production variant    Remove - Delete the variant    Retain - Keep the variant as it is  
     */
    ModelVariantActions: ModelVariantActionMap;
    /**
     *  An array of ModelVariantConfig objects. There is one for each variant that you want to deploy after the inference experiment stops. Each ModelVariantConfig describes the infrastructure configuration for deploying the corresponding variant. 
     */
    DesiredModelVariants?: ModelVariantConfigList;
    /**
     *  The desired state of the experiment after stopping. The possible states are the following:     Completed: The experiment completed successfully    Cancelled: The experiment was canceled  
     */
    DesiredState?: InferenceExperimentStopDesiredState;
    /**
     * The reason for stopping the experiment.
     */
    Reason?: InferenceExperimentStatusReason;
  }
  export interface StopInferenceExperimentResponse {
    /**
     * The ARN of the stopped inference experiment.
     */
    InferenceExperimentArn: InferenceExperimentArn;
  }
  export interface StopInferenceRecommendationsJobRequest {
    /**
     * The name of the job you want to stop.
     */
    JobName: RecommendationJobName;
  }
  export interface StopLabelingJobRequest {
    /**
     * The name of the labeling job to stop.
     */
    LabelingJobName: LabelingJobName;
  }
  export interface StopMlflowTrackingServerRequest {
    /**
     * The name of the tracking server to stop.
     */
    TrackingServerName: TrackingServerName;
  }
  export interface StopMlflowTrackingServerResponse {
    /**
     * The ARN of the stopped tracking server.
     */
    TrackingServerArn?: TrackingServerArn;
  }
  export interface StopMonitoringScheduleRequest {
    /**
     * The name of the schedule to stop.
     */
    MonitoringScheduleName: MonitoringScheduleName;
  }
  export interface StopNotebookInstanceInput {
    /**
     * The name of the notebook instance to terminate.
     */
    NotebookInstanceName: NotebookInstanceName;
  }
  export interface StopOptimizationJobRequest {
    /**
     * The name that you assigned to the optimization job.
     */
    OptimizationJobName: EntityName;
  }
  export interface StopPipelineExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
    /**
     * A unique, case-sensitive identifier that you provide to ensure the idempotency of the operation. An idempotent operation completes no more than once.
     */
    ClientRequestToken: IdempotencyToken;
  }
  export interface StopPipelineExecutionResponse {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface StopProcessingJobRequest {
    /**
     * The name of the processing job to stop.
     */
    ProcessingJobName: ProcessingJobName;
  }
  export interface StopTrainingJobRequest {
    /**
     * The name of the training job to stop.
     */
    TrainingJobName: TrainingJobName;
  }
  export interface StopTransformJobRequest {
    /**
     * The name of the batch transform job to stop.
     */
    TransformJobName: TransformJobName;
  }
  export interface StoppingCondition {
    /**
     * The maximum length of time, in seconds, that a training or compilation job can run before it is stopped. For compilation jobs, if the job does not complete during this time, a TimeOut error is generated. We recommend starting with 900 seconds and increasing as necessary based on your model. For all other jobs, if the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxRuntimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt. The default value is 1 day. The maximum value is 28 days. The maximum time that a TrainingJob can run in total, including any time spent publishing metrics or archiving and uploading models after it has been stopped, is 30 days.
     */
    MaxRuntimeInSeconds?: MaxRuntimeInSeconds;
    /**
     * The maximum length of time, in seconds, that a managed Spot training job has to complete. It is the amount of time spent waiting for Spot capacity plus the amount of time the job can run. It must be equal to or greater than MaxRuntimeInSeconds. If the job does not complete during this time, SageMaker ends the job. When RetryStrategy is specified in the job request, MaxWaitTimeInSeconds specifies the maximum time for all of the attempts in total, not each individual attempt.
     */
    MaxWaitTimeInSeconds?: MaxWaitTimeInSeconds;
    /**
     * The maximum length of time, in seconds, that a training or compilation job can be pending before it is stopped.
     */
    MaxPendingTimeInSeconds?: MaxPendingTimeInSeconds;
  }
  export type StorageType = "Standard"|"InMemory"|string;
  export type String = string;
  export type String1024 = string;
  export type String128 = string;
  export type String200 = string;
  export type String256 = string;
  export type String3072 = string;
  export type String40 = string;
  export type String64 = string;
  export type String8192 = string;
  export type StringParameterValue = string;
  export type StudioLifecycleConfigAppType = "JupyterServer"|"KernelGateway"|"CodeEditor"|"JupyterLab"|string;
  export type StudioLifecycleConfigArn = string;
  export type StudioLifecycleConfigContent = string;
  export interface StudioLifecycleConfigDetails {
    /**
     *  The Amazon Resource Name (ARN) of the Lifecycle Configuration.
     */
    StudioLifecycleConfigArn?: StudioLifecycleConfigArn;
    /**
     * The name of the Amazon SageMaker Studio Lifecycle Configuration.
     */
    StudioLifecycleConfigName?: StudioLifecycleConfigName;
    /**
     * The creation time of the Amazon SageMaker Studio Lifecycle Configuration.
     */
    CreationTime?: Timestamp;
    /**
     * This value is equivalent to CreationTime because Amazon SageMaker Studio Lifecycle Configurations are immutable.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The App type to which the Lifecycle Configuration is attached.
     */
    StudioLifecycleConfigAppType?: StudioLifecycleConfigAppType;
  }
  export type StudioLifecycleConfigName = string;
  export type StudioLifecycleConfigSortKey = "CreationTime"|"LastModifiedTime"|"Name"|string;
  export type StudioLifecycleConfigsList = StudioLifecycleConfigDetails[];
  export type StudioWebPortal = "ENABLED"|"DISABLED"|string;
  export interface StudioWebPortalSettings {
    /**
     * The machine learning tools that are hidden from the Studio left navigation pane.
     */
    HiddenMlTools?: HiddenMlToolsList;
    /**
     * The Applications supported in Studio that are hidden from the Studio left navigation pane.
     */
    HiddenAppTypes?: HiddenAppTypesList;
  }
  export type SubnetId = string;
  export type Subnets = SubnetId[];
  export interface SubscribedWorkteam {
    /**
     * The Amazon Resource Name (ARN) of the vendor that you have subscribed.
     */
    WorkteamArn: WorkteamArn;
    /**
     * The title of the service provided by the vendor in the Amazon Marketplace.
     */
    MarketplaceTitle?: String200;
    /**
     * The name of the vendor in the Amazon Marketplace.
     */
    SellerName?: String;
    /**
     * The description of the vendor from the Amazon Marketplace.
     */
    MarketplaceDescription?: String200;
    /**
     * Marketplace product listing ID.
     */
    ListingId?: String;
  }
  export type SubscribedWorkteams = SubscribedWorkteam[];
  export type Success = boolean;
  export interface SuggestionQuery {
    /**
     * Defines a property name hint. Only property names that begin with the specified hint are included in the response.
     */
    PropertyNameQuery?: PropertyNameQuery;
  }
  export type TableFormat = "Default"|"Glue"|"Iceberg"|string;
  export type TableName = string;
  export interface TabularJobConfig {
    /**
     * The configuration information of how model candidates are generated.
     */
    CandidateGenerationConfig?: CandidateGenerationConfig;
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * A URL to the Amazon S3 data source containing selected features from the input data source to run an Autopilot job V2. You can input FeatureAttributeNames (optional) in JSON format as shown below:   { "FeatureAttributeNames":["col1", "col2", ...] }. You can also specify the data type of the feature (optional) in the format shown below:  { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }   These column keys may not include the target column.  In ensembling mode, Autopilot only supports the following data types: numeric, categorical, text, and datetime. In HPO mode, Autopilot can support numeric, categorical, text, datetime, and sequence. If only FeatureDataTypes is provided, the column keys (col1, col2,..) should be a subset of the column names in the input data.  If both FeatureDataTypes and FeatureAttributeNames are provided, then the column keys should be a subset of the column names provided in FeatureAttributeNames.  The key name FeatureAttributeNames is fixed. The values listed in ["col1", "col2", ...] are case sensitive and should be a list of strings containing unique values that are a subset of the column names in the input data. The list of columns provided must not include the target column.
     */
    FeatureSpecificationS3Uri?: S3Uri;
    /**
     * The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones. The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode. The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.
     */
    Mode?: AutoMLMode;
    /**
     * Generates possible candidates without training the models. A model candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
     */
    GenerateCandidateDefinitionsOnly?: GenerateCandidateDefinitionsOnly;
    /**
     * The type of supervised learning problem available for the model candidates of the AutoML job V2. For more information, see  SageMaker Autopilot problem types.  You must either specify the type of supervised learning problem in ProblemType and provide the AutoMLJobObjective metric, or none at all. 
     */
    ProblemType?: ProblemType;
    /**
     * The name of the target variable in supervised learning, usually represented by 'y'.
     */
    TargetAttributeName: TargetAttributeName;
    /**
     * If specified, this column name indicates which column of the dataset should be treated as sample weights for use by the objective metric during the training, evaluation, and the selection of the best model. This column is not considered as a predictive feature. For more information on Autopilot metrics, see Metrics and validation. Sample weights should be numeric, non-negative, with larger values indicating which rows are more important than others. Data points that have invalid or no weight value are excluded. Support for sample weights is available in Ensembling mode only.
     */
    SampleWeightAttributeName?: SampleWeightAttributeName;
  }
  export interface TabularResolvedAttributes {
    /**
     * The type of supervised learning problem available for the model candidates of the AutoML job V2 (Binary Classification, Multiclass Classification, Regression). For more information, see  SageMaker Autopilot problem types.
     */
    ProblemType?: ProblemType;
  }
  export interface Tag {
    /**
     * The tag key. Tag keys must be unique per resource.
     */
    Key: TagKey;
    /**
     * The tag value.
     */
    Value: TagValue;
  }
  export type TagKey = string;
  export type TagKeyList = TagKey[];
  export type TagList = Tag[];
  export type TagValue = string;
  export type TargetAttributeName = string;
  export type TargetDevice = "lambda"|"ml_m4"|"ml_m5"|"ml_m6g"|"ml_c4"|"ml_c5"|"ml_c6g"|"ml_p2"|"ml_p3"|"ml_g4dn"|"ml_inf1"|"ml_inf2"|"ml_trn1"|"ml_eia2"|"jetson_tx1"|"jetson_tx2"|"jetson_nano"|"jetson_xavier"|"rasp3b"|"rasp4b"|"imx8qm"|"deeplens"|"rk3399"|"rk3288"|"aisage"|"sbe_c"|"qcs605"|"qcs603"|"sitara_am57x"|"amba_cv2"|"amba_cv22"|"amba_cv25"|"x86_win32"|"x86_win64"|"coreml"|"jacinto_tda4vm"|"imx8mplus"|string;
  export type TargetLabelColumn = string;
  export type TargetObjectiveMetricValue = number;
  export interface TargetPlatform {
    /**
     * Specifies a target platform OS.    LINUX: Linux-based operating systems.    ANDROID: Android operating systems. Android API level can be specified using the ANDROID_PLATFORM compiler option. For example, "CompilerOptions": {'ANDROID_PLATFORM': 28}   
     */
    Os: TargetPlatformOs;
    /**
     * Specifies a target platform architecture.    X86_64: 64-bit version of the x86 instruction set.    X86: 32-bit version of the x86 instruction set.    ARM64: ARMv8 64-bit CPU.    ARM_EABIHF: ARMv7 32-bit, Hard Float.    ARM_EABI: ARMv7 32-bit, Soft Float. Used by Android 32-bit ARM platform.  
     */
    Arch: TargetPlatformArch;
    /**
     * Specifies a target platform accelerator (optional).    NVIDIA: Nvidia graphics processing unit. It also requires gpu-code, trt-ver, cuda-ver compiler options    MALI: ARM Mali graphics processor    INTEL_GRAPHICS: Integrated Intel graphics  
     */
    Accelerator?: TargetPlatformAccelerator;
  }
  export type TargetPlatformAccelerator = "INTEL_GRAPHICS"|"MALI"|"NVIDIA"|"NNA"|string;
  export type TargetPlatformArch = "X86_64"|"X86"|"ARM64"|"ARM_EABI"|"ARM_EABIHF"|string;
  export type TargetPlatformOs = "ANDROID"|"LINUX"|string;
  export interface TargetTrackingScalingPolicyConfiguration {
    /**
     * An object containing information about a metric.
     */
    MetricSpecification?: MetricSpecification;
    /**
     * The recommended target value to specify for the metric when creating a scaling policy.
     */
    TargetValue?: Double;
  }
  export type TaskAvailabilityLifetimeInSeconds = number;
  export type TaskCount = number;
  export type TaskDescription = string;
  export type TaskInput = string;
  export type TaskKeyword = string;
  export type TaskKeywords = TaskKeyword[];
  export type TaskTimeLimitInSeconds = number;
  export type TaskTitle = string;
  export type TemplateContent = string;
  export type TemplateContentSha256 = string;
  export type TemplateUrl = string;
  export interface TensorBoardAppSettings {
    /**
     * The default instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.
     */
    DefaultResourceSpec?: ResourceSpec;
  }
  export interface TensorBoardOutputConfig {
    /**
     * Path to local storage location for tensorBoard output. Defaults to /opt/ml/output/tensorboard.
     */
    LocalPath?: DirectoryPath;
    /**
     * Path to Amazon S3 storage location for TensorBoard output.
     */
    S3OutputPath: S3Uri;
  }
  export type TenthFractionsOfACent = number;
  export type TerminationWaitInSeconds = number;
  export interface TextClassificationJobConfig {
    /**
     * How long a job is allowed to run, or how many candidates a job is allowed to generate.
     */
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * The name of the column used to provide the sentences to be classified. It should not be the same as the target column.
     */
    ContentColumn: ContentColumn;
    /**
     * The name of the column used to provide the class labels. It should not be same as the content column.
     */
    TargetLabelColumn: TargetLabelColumn;
  }
  export type TextGenerationHyperParameterKey = string;
  export type TextGenerationHyperParameterValue = string;
  export type TextGenerationHyperParameters = {[key: string]: TextGenerationHyperParameterValue};
  export interface TextGenerationJobConfig {
    /**
     * How long a fine-tuning job is allowed to run. For TextGenerationJobConfig problem types, the MaxRuntimePerTrainingJobInSeconds attribute of AutoMLJobCompletionCriteria defaults to 72h (259200s).
     */
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * The name of the base model to fine-tune. Autopilot supports fine-tuning a variety of large language models. For information on the list of supported models, see Text generation models supporting fine-tuning in Autopilot. If no BaseModelName is provided, the default model used is Falcon7BInstruct. 
     */
    BaseModelName?: BaseModelName;
    /**
     * The hyperparameters used to configure and optimize the learning process of the base model. You can set any combination of the following hyperparameters for all base models. For more information on each supported hyperparameter, see Optimize the learning process of your text generation models with hyperparameters.    "epochCount": The number of times the model goes through the entire training dataset. Its value should be a string containing an integer value within the range of "1" to "10".    "batchSize": The number of data samples used in each iteration of training. Its value should be a string containing an integer value within the range of "1" to "64".    "learningRate": The step size at which a model's parameters are updated during training. Its value should be a string containing a floating-point value within the range of "0" to "1".    "learningRateWarmupSteps": The number of training steps during which the learning rate gradually increases before reaching its target or maximum value. Its value should be a string containing an integer value within the range of "0" to "250".   Here is an example where all four hyperparameters are configured.  { "epochCount":"5", "learningRate":"0.5", "batchSize": "32", "learningRateWarmupSteps": "10" } 
     */
    TextGenerationHyperParameters?: TextGenerationHyperParameters;
    ModelAccessConfig?: ModelAccessConfig;
  }
  export interface TextGenerationResolvedAttributes {
    /**
     * The name of the base model to fine-tune.
     */
    BaseModelName?: BaseModelName;
  }
  export type ThingName = string;
  export interface ThroughputConfig {
    /**
     * The mode used for your feature group throughput: ON_DEMAND or PROVISIONED. 
     */
    ThroughputMode: ThroughputMode;
    /**
     *  For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.  This field is not applicable for on-demand feature groups. 
     */
    ProvisionedReadCapacityUnits?: CapacityUnit;
    /**
     *  For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.  This field is not applicable for on-demand feature groups. 
     */
    ProvisionedWriteCapacityUnits?: CapacityUnit;
  }
  export interface ThroughputConfigDescription {
    /**
     * The mode used for your feature group throughput: ON_DEMAND or PROVISIONED. 
     */
    ThroughputMode: ThroughputMode;
    /**
     *  For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling.  This field is not applicable for on-demand feature groups. 
     */
    ProvisionedReadCapacityUnits?: CapacityUnit;
    /**
     *  For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling.  This field is not applicable for on-demand feature groups. 
     */
    ProvisionedWriteCapacityUnits?: CapacityUnit;
  }
  export interface ThroughputConfigUpdate {
    /**
     * Target throughput mode of the feature group. Throughput update is an asynchronous operation, and the outcome should be monitored by polling LastUpdateStatus field in DescribeFeatureGroup response. You cannot update a feature group's throughput while another update is in progress. 
     */
    ThroughputMode?: ThroughputMode;
    /**
     * For provisioned feature groups with online store enabled, this indicates the read throughput you are billed for and can consume without throttling. 
     */
    ProvisionedReadCapacityUnits?: CapacityUnit;
    /**
     * For provisioned feature groups, this indicates the write throughput you are billed for and can consume without throttling. 
     */
    ProvisionedWriteCapacityUnits?: CapacityUnit;
  }
  export type ThroughputMode = "OnDemand"|"Provisioned"|string;
  export interface TimeSeriesConfig {
    /**
     * The name of the column representing the target variable that you want to predict for each item in your dataset. The data type of the target variable must be numerical.
     */
    TargetAttributeName: TargetAttributeName;
    /**
     * The name of the column indicating a point in time at which the target value of a given item is recorded.
     */
    TimestampAttributeName: TimestampAttributeName;
    /**
     * The name of the column that represents the set of item identifiers for which you want to predict the target value.
     */
    ItemIdentifierAttributeName: ItemIdentifierAttributeName;
    /**
     * A set of columns names that can be grouped with the item identifier column to create a composite key for which a target value is predicted.
     */
    GroupingAttributeNames?: GroupingAttributeNames;
  }
  export interface TimeSeriesForecastingJobConfig {
    /**
     * A URL to the Amazon S3 data source containing additional selected features that complement the target, itemID, timestamp, and grouped columns set in TimeSeriesConfig. When not provided, the AutoML job V2 includes all the columns from the original dataset that are not already declared in TimeSeriesConfig. If provided, the AutoML job V2 only considers these additional columns as a complement to the ones declared in TimeSeriesConfig. You can input FeatureAttributeNames (optional) in JSON format as shown below:   { "FeatureAttributeNames":["col1", "col2", ...] }. You can also specify the data type of the feature (optional) in the format shown below:  { "FeatureDataTypes":{"col1":"numeric", "col2":"categorical" ... } }  Autopilot supports the following data types: numeric, categorical, text, and datetime.  These column keys must not include any column set in TimeSeriesConfig. 
     */
    FeatureSpecificationS3Uri?: S3Uri;
    CompletionCriteria?: AutoMLJobCompletionCriteria;
    /**
     * The frequency of predictions in a forecast. Valid intervals are an integer followed by Y (Year), M (Month), W (Week), D (Day), H (Hour), and min (Minute). For example, 1D indicates every day and 15min indicates every 15 minutes. The value of a frequency must not overlap with the next larger frequency. For example, you must use a frequency of 1H instead of 60min. The valid values for each frequency are the following:   Minute - 1-59   Hour - 1-23   Day - 1-6   Week - 1-4   Month - 1-11   Year - 1  
     */
    ForecastFrequency: ForecastFrequency;
    /**
     * The number of time-steps that the model predicts. The forecast horizon is also called the prediction length. The maximum forecast horizon is the lesser of 500 time-steps or 1/4 of the time-steps in the dataset.
     */
    ForecastHorizon: ForecastHorizon;
    /**
     * The quantiles used to train the model for forecasts at a specified quantile. You can specify quantiles from 0.01 (p1) to 0.99 (p99), by increments of 0.01 or higher. Up to five forecast quantiles can be specified. When ForecastQuantiles is not provided, the AutoML job uses the quantiles p10, p50, and p90 as default.
     */
    ForecastQuantiles?: ForecastQuantiles;
    /**
     * The transformations modifying specific attributes of the time-series, such as filling strategies for missing values.
     */
    Transformations?: TimeSeriesTransformations;
    /**
     * The collection of components that defines the time-series.
     */
    TimeSeriesConfig: TimeSeriesConfig;
    /**
     * The collection of holiday featurization attributes used to incorporate national holiday information into your forecasting model.
     */
    HolidayConfig?: HolidayConfig;
    CandidateGenerationConfig?: CandidateGenerationConfig;
  }
  export interface TimeSeriesForecastingSettings {
    /**
     * Describes whether time series forecasting is enabled or disabled in the Canvas application.
     */
    Status?: FeatureStatus;
    /**
     * The IAM role that Canvas passes to Amazon Forecast for time series forecasting. By default, Canvas uses the execution role specified in the UserProfile that launches the Canvas application. If an execution role is not specified in the UserProfile, Canvas uses the execution role specified in the Domain that owns the UserProfile. To allow time series forecasting, this IAM role should have the  AmazonSageMakerCanvasForecastAccess policy attached and forecast.amazonaws.com added in the trust relationship as a service principal.
     */
    AmazonForecastRoleArn?: RoleArn;
  }
  export interface TimeSeriesTransformations {
    /**
     * A key value pair defining the filling method for a column, where the key is the column name and the value is an object which defines the filling logic. You can specify multiple filling methods for a single column. The supported filling methods and their corresponding options are:    frontfill: none (Supported only for target column)    middlefill: zero, value, median, mean, min, max     backfill: zero, value, median, mean, min, max     futurefill: zero, value, median, mean, min, max    To set a filling method to a specific value, set the fill parameter to the chosen filling method value (for example "backfill" : "value"), and define the filling value in an additional parameter prefixed with "_value". For example, to set backfill to a value of 2, you must include two parameters: "backfill": "value" and "backfill_value":"2".
     */
    Filling?: FillingTransformations;
    /**
     * A key value pair defining the aggregation method for a column, where the key is the column name and the value is the aggregation method. The supported aggregation methods are sum (default), avg, first, min, max.  Aggregation is only supported for the target column. 
     */
    Aggregation?: AggregationTransformations;
  }
  export type Timestamp = Date;
  export type TimestampAttributeName = string;
  export type TrackingServerArn = string;
  export type TrackingServerName = string;
  export type TrackingServerSize = "Small"|"Medium"|"Large"|string;
  export type TrackingServerStatus = "Creating"|"Created"|"CreateFailed"|"Updating"|"Updated"|"UpdateFailed"|"Deleting"|"DeleteFailed"|"Stopping"|"Stopped"|"StopFailed"|"Starting"|"Started"|"StartFailed"|"MaintenanceInProgress"|"MaintenanceComplete"|"MaintenanceFailed"|string;
  export interface TrackingServerSummary {
    /**
     * The ARN of a listed tracking server.
     */
    TrackingServerArn?: TrackingServerArn;
    /**
     * The name of a listed tracking server.
     */
    TrackingServerName?: TrackingServerName;
    /**
     * The creation time of a listed tracking server.
     */
    CreationTime?: Timestamp;
    /**
     * The last modified time of a listed tracking server.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The creation status of a listed tracking server.
     */
    TrackingServerStatus?: TrackingServerStatus;
    /**
     * The activity status of a listed tracking server.
     */
    IsActive?: IsTrackingServerActive;
    /**
     * The MLflow version used for a listed tracking server.
     */
    MlflowVersion?: MlflowVersion;
  }
  export type TrackingServerSummaryList = TrackingServerSummary[];
  export type TrackingServerUrl = string;
  export type TrafficDurationInSeconds = number;
  export interface TrafficPattern {
    /**
     * Defines the traffic patterns. Choose either PHASES or STAIRS.
     */
    TrafficType?: TrafficType;
    /**
     * Defines the phases traffic specification.
     */
    Phases?: Phases;
    /**
     * Defines the stairs traffic pattern.
     */
    Stairs?: Stairs;
  }
  export interface TrafficRoutingConfig {
    /**
     * Traffic routing strategy type.    ALL_AT_ONCE: Endpoint traffic shifts to the new fleet in a single step.     CANARY: Endpoint traffic shifts to the new fleet in two steps. The first step is the canary, which is a small portion of the traffic. The second step is the remainder of the traffic.     LINEAR: Endpoint traffic shifts to the new fleet in n steps of a configurable size.   
     */
    Type: TrafficRoutingConfigType;
    /**
     * The waiting time (in seconds) between incremental steps to turn on traffic on the new endpoint fleet.
     */
    WaitIntervalInSeconds: WaitIntervalInSeconds;
    /**
     * Batch size for the first step to turn on traffic on the new endpoint fleet. Value must be less than or equal to 50% of the variant's total instance count.
     */
    CanarySize?: CapacitySize;
    /**
     * Batch size for each step to turn on traffic on the new endpoint fleet. Value must be 10-50% of the variant's total instance count.
     */
    LinearStepSize?: CapacitySize;
  }
  export type TrafficRoutingConfigType = "ALL_AT_ONCE"|"CANARY"|"LINEAR"|string;
  export type TrafficType = "PHASES"|"STAIRS"|string;
  export type TrainingContainerArgument = string;
  export type TrainingContainerArguments = TrainingContainerArgument[];
  export type TrainingContainerEntrypoint = TrainingContainerEntrypointString[];
  export type TrainingContainerEntrypointString = string;
  export type TrainingEnvironmentKey = string;
  export type TrainingEnvironmentMap = {[key: string]: TrainingEnvironmentValue};
  export type TrainingEnvironmentValue = string;
  export interface TrainingImageConfig {
    /**
     * The method that your training job will use to gain access to the images in your private Docker registry. For access to an image in a private Docker registry, set to Vpc.
     */
    TrainingRepositoryAccessMode: TrainingRepositoryAccessMode;
    /**
     * An object containing authentication information for a private Docker registry containing your training images.
     */
    TrainingRepositoryAuthConfig?: TrainingRepositoryAuthConfig;
  }
  export type TrainingInputMode = "Pipe"|"File"|"FastFile"|string;
  export type TrainingInstanceCount = number;
  export type TrainingInstanceType = "ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.p3dn.24xlarge"|"ml.p4d.24xlarge"|"ml.p4de.24xlarge"|"ml.p5.48xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.c5n.xlarge"|"ml.c5n.2xlarge"|"ml.c5n.4xlarge"|"ml.c5n.9xlarge"|"ml.c5n.18xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.16xlarge"|"ml.g5.12xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|"ml.trn1.2xlarge"|"ml.trn1.32xlarge"|"ml.trn1n.32xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.8xlarge"|"ml.c6i.4xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.r5d.large"|"ml.r5d.xlarge"|"ml.r5d.2xlarge"|"ml.r5d.4xlarge"|"ml.r5d.8xlarge"|"ml.r5d.12xlarge"|"ml.r5d.16xlarge"|"ml.r5d.24xlarge"|"ml.t3.medium"|"ml.t3.large"|"ml.t3.xlarge"|"ml.t3.2xlarge"|"ml.r5.large"|"ml.r5.xlarge"|"ml.r5.2xlarge"|"ml.r5.4xlarge"|"ml.r5.8xlarge"|"ml.r5.12xlarge"|"ml.r5.16xlarge"|"ml.r5.24xlarge"|string;
  export type TrainingInstanceTypes = TrainingInstanceType[];
  export interface TrainingJob {
    /**
     * The name of the training job.
     */
    TrainingJobName?: TrainingJobName;
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn?: TrainingJobArn;
    /**
     * The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.
     */
    TuningJobArn?: HyperParameterTuningJobArn;
    /**
     * The Amazon Resource Name (ARN) of the labeling job.
     */
    LabelingJobArn?: LabelingJobArn;
    /**
     * The Amazon Resource Name (ARN) of the job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    /**
     * Information about the Amazon S3 location that is configured for storing model artifacts.
     */
    ModelArtifacts?: ModelArtifacts;
    /**
     * The status of the training job. Training job statuses are:    InProgress - The training is in progress.    Completed - The training job has completed.    Failed - The training job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTrainingJobResponse call.    Stopping - The training job is stopping.    Stopped - The training job has stopped.   For more detailed information, see SecondaryStatus. 
     */
    TrainingJobStatus?: TrainingJobStatus;
    /**
     *  Provides detailed information about the state of the training job. For detailed information about the secondary status of the training job, see StatusMessage under SecondaryStatusTransition. SageMaker provides primary statuses and secondary statuses that apply to each of them:  InProgress     Starting - Starting the training job.    Downloading - An optional stage for algorithms that support File training input mode. It indicates that data is being downloaded to the ML storage volumes.    Training - Training is in progress.    Uploading - Training is complete and the model artifacts are being uploaded to the S3 location.    Completed     Completed - The training job has completed.    Failed     Failed - The training job has failed. The reason for the failure is returned in the FailureReason field of DescribeTrainingJobResponse.    Stopped     MaxRuntimeExceeded - The job stopped because it exceeded the maximum allowed runtime.    Stopped - The training job has stopped.    Stopping     Stopping - Stopping the training job.      Valid values for SecondaryStatus are subject to change.   We no longer support the following secondary statuses:    LaunchingMLInstances     PreparingTrainingStack     DownloadingTrainingImage   
     */
    SecondaryStatus?: SecondaryStatus;
    /**
     * If the training job failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * Algorithm-specific parameters.
     */
    HyperParameters?: HyperParameters;
    /**
     * Information about the algorithm used for training, and algorithm metadata.
     */
    AlgorithmSpecification?: AlgorithmSpecification;
    /**
     * The Amazon Web Services Identity and Access Management (IAM) role configured for the training job.
     */
    RoleArn?: RoleArn;
    /**
     * An array of Channel objects that describes each data input channel. Your input must be in the same Amazon Web Services region as your training job.
     */
    InputDataConfig?: InputDataConfig;
    /**
     * The S3 path where model artifacts that you configured when creating the job are stored. SageMaker creates subfolders for model artifacts.
     */
    OutputDataConfig?: OutputDataConfig;
    /**
     * Resources, including ML compute instances and ML storage volumes, that are configured for model training.
     */
    ResourceConfig?: ResourceConfig;
    /**
     * A VpcConfig object that specifies the VPC that this training job has access to. For more information, see Protect Training Jobs by Using an Amazon Virtual Private Cloud.
     */
    VpcConfig?: VpcConfig;
    /**
     * Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts, so the results of training are not lost. 
     */
    StoppingCondition?: StoppingCondition;
    /**
     * A timestamp that indicates when the training job was created.
     */
    CreationTime?: Timestamp;
    /**
     * Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.
     */
    TrainingStartTime?: Timestamp;
    /**
     * Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when SageMaker detects a job failure.
     */
    TrainingEndTime?: Timestamp;
    /**
     * A timestamp that indicates when the status of the training job was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * A history of all of the secondary statuses that the training job has transitioned through.
     */
    SecondaryStatusTransitions?: SecondaryStatusTransitions;
    /**
     * A list of final metric values that are set when the training job completes. Used only if the training job was configured to use metrics.
     */
    FinalMetricDataList?: FinalMetricDataList;
    /**
     * If the TrainingJob was created with network isolation, the value is set to true. If network isolation is enabled, nodes can't communicate beyond the VPC they run in.
     */
    EnableNetworkIsolation?: Boolean;
    /**
     * To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training.
     */
    EnableInterContainerTrafficEncryption?: Boolean;
    /**
     * When true, enables managed spot training using Amazon EC2 Spot instances to run training jobs instead of on-demand instances. For more information, see Managed Spot Training.
     */
    EnableManagedSpotTraining?: Boolean;
    CheckpointConfig?: CheckpointConfig;
    /**
     * The training time in seconds.
     */
    TrainingTimeInSeconds?: TrainingTimeInSeconds;
    /**
     * The billable time in seconds.
     */
    BillableTimeInSeconds?: BillableTimeInSeconds;
    DebugHookConfig?: DebugHookConfig;
    ExperimentConfig?: ExperimentConfig;
    /**
     * Information about the debug rule configuration.
     */
    DebugRuleConfigurations?: DebugRuleConfigurations;
    TensorBoardOutputConfig?: TensorBoardOutputConfig;
    /**
     * Information about the evaluation status of the rules for the training job.
     */
    DebugRuleEvaluationStatuses?: DebugRuleEvaluationStatuses;
    ProfilerConfig?: ProfilerConfig;
    /**
     * The environment variables to set in the Docker container.
     */
    Environment?: TrainingEnvironmentMap;
    /**
     * The number of times to retry the job when the job fails due to an InternalServerError.
     */
    RetryStrategy?: RetryStrategy;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
     */
    Tags?: TagList;
  }
  export type TrainingJobArn = string;
  export interface TrainingJobDefinition {
    TrainingInputMode: TrainingInputMode;
    /**
     * The hyperparameters used for the training job.
     */
    HyperParameters?: HyperParameters;
    /**
     * An array of Channel objects, each of which specifies an input source.
     */
    InputDataConfig: InputDataConfig;
    /**
     * the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
     */
    OutputDataConfig: OutputDataConfig;
    /**
     * The resources, including the ML compute instances and ML storage volumes, to use for model training.
     */
    ResourceConfig: ResourceConfig;
    /**
     * Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
     */
    StoppingCondition: StoppingCondition;
  }
  export type TrainingJobEarlyStoppingType = "Off"|"Auto"|string;
  export type TrainingJobName = string;
  export type TrainingJobSortByOptions = "Name"|"CreationTime"|"Status"|"FinalObjectiveMetricValue"|string;
  export type TrainingJobStatus = "InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export type TrainingJobStatusCounter = number;
  export interface TrainingJobStatusCounters {
    /**
     * The number of completed training jobs launched by the hyperparameter tuning job.
     */
    Completed?: TrainingJobStatusCounter;
    /**
     * The number of in-progress training jobs launched by a hyperparameter tuning job.
     */
    InProgress?: TrainingJobStatusCounter;
    /**
     * The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.
     */
    RetryableError?: TrainingJobStatusCounter;
    /**
     * The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.
     */
    NonRetryableError?: TrainingJobStatusCounter;
    /**
     * The number of training jobs launched by a hyperparameter tuning job that were manually stopped.
     */
    Stopped?: TrainingJobStatusCounter;
  }
  export interface TrainingJobStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the training job that was run by this step execution.
     */
    Arn?: TrainingJobArn;
  }
  export type TrainingJobSummaries = TrainingJobSummary[];
  export interface TrainingJobSummary {
    /**
     * The name of the training job that you want a summary for.
     */
    TrainingJobName: TrainingJobName;
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn: TrainingJobArn;
    /**
     * A timestamp that shows when the training job was created.
     */
    CreationTime: Timestamp;
    /**
     * A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed, Failed, or Stopped). 
     */
    TrainingEndTime?: Timestamp;
    /**
     *  Timestamp when the training job was last modified. 
     */
    LastModifiedTime?: Timestamp;
    /**
     * The status of the training job.
     */
    TrainingJobStatus: TrainingJobStatus;
    /**
     * The status of the warm pool associated with the training job.
     */
    WarmPoolStatus?: WarmPoolStatus;
  }
  export type TrainingRepositoryAccessMode = "Platform"|"Vpc"|string;
  export interface TrainingRepositoryAuthConfig {
    /**
     * The Amazon Resource Name (ARN) of an Amazon Web Services Lambda function used to give SageMaker access credentials to your private Docker registry.
     */
    TrainingRepositoryCredentialsProviderArn: TrainingRepositoryCredentialsProviderArn;
  }
  export type TrainingRepositoryCredentialsProviderArn = string;
  export interface TrainingSpecification {
    /**
     * The Amazon ECR registry path of the Docker image that contains the training algorithm.
     */
    TrainingImage: ContainerImage;
    /**
     * An MD5 hash of the training algorithm that identifies the Docker image used for training.
     */
    TrainingImageDigest?: ImageDigest;
    /**
     * A list of the HyperParameterSpecification objects, that define the supported hyperparameters. This is required if the algorithm supports automatic model tuning.&gt;
     */
    SupportedHyperParameters?: HyperParameterSpecifications;
    /**
     * A list of the instance types that this algorithm can use for training.
     */
    SupportedTrainingInstanceTypes: TrainingInstanceTypes;
    /**
     * Indicates whether the algorithm supports distributed training. If set to false, buyers can't request more than one instance during training.
     */
    SupportsDistributedTraining?: Boolean;
    /**
     * A list of MetricDefinition objects, which are used for parsing metrics generated by the algorithm.
     */
    MetricDefinitions?: MetricDefinitionList;
    /**
     * A list of ChannelSpecification objects, which specify the input sources to be used by the algorithm.
     */
    TrainingChannels: ChannelSpecifications;
    /**
     * A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
     */
    SupportedTuningJobObjectiveMetrics?: HyperParameterTuningJobObjectives;
    /**
     * The additional data source used during the training job.
     */
    AdditionalS3DataSource?: AdditionalS3DataSource;
  }
  export type TrainingTimeInSeconds = number;
  export interface TransformDataSource {
    /**
     * The S3 location of the data source that is associated with a channel.
     */
    S3DataSource: TransformS3DataSource;
  }
  export type TransformEnvironmentKey = string;
  export type TransformEnvironmentMap = {[key: string]: TransformEnvironmentValue};
  export type TransformEnvironmentValue = string;
  export interface TransformInput {
    /**
     * Describes the location of the channel data, which is, the S3 location of the input data that the model can consume.
     */
    DataSource: TransformDataSource;
    /**
     * The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.
     */
    ContentType?: ContentType;
    /**
     * If your transform data is compressed, specify the compression type. Amazon SageMaker automatically decompresses the data for the transform job accordingly. The default value is None.
     */
    CompressionType?: CompressionType;
    /**
     * The method to use to split the transform job's data files into smaller batches. Splitting is necessary when the total size of each object is too large to fit in a single request. You can also use data splitting to improve performance by processing multiple concurrent mini-batches. The default value for SplitType is None, which indicates that input data files are not split, and request payloads contain the entire contents of an input object. Set the value of this parameter to Line to split records on a newline character boundary. SplitType also supports a number of record-oriented binary data formats. Currently, the supported record formats are:   RecordIO   TFRecord   When splitting is enabled, the size of a mini-batch depends on the values of the BatchStrategy and MaxPayloadInMB parameters. When the value of BatchStrategy is MultiRecord, Amazon SageMaker sends the maximum number of records in each request, up to the MaxPayloadInMB limit. If the value of BatchStrategy is SingleRecord, Amazon SageMaker sends individual records in each request.  Some data formats represent a record as a binary payload wrapped with extra padding bytes. When splitting is applied to a binary data format, padding is removed if the value of BatchStrategy is set to SingleRecord. Padding is not removed if the value of BatchStrategy is set to MultiRecord. For more information about RecordIO, see Create a Dataset Using RecordIO in the MXNet documentation. For more information about TFRecord, see Consuming TFRecord data in the TensorFlow documentation. 
     */
    SplitType?: SplitType;
  }
  export type TransformInstanceCount = number;
  export type TransformInstanceType = "ml.m4.xlarge"|"ml.m4.2xlarge"|"ml.m4.4xlarge"|"ml.m4.10xlarge"|"ml.m4.16xlarge"|"ml.c4.xlarge"|"ml.c4.2xlarge"|"ml.c4.4xlarge"|"ml.c4.8xlarge"|"ml.p2.xlarge"|"ml.p2.8xlarge"|"ml.p2.16xlarge"|"ml.p3.2xlarge"|"ml.p3.8xlarge"|"ml.p3.16xlarge"|"ml.c5.xlarge"|"ml.c5.2xlarge"|"ml.c5.4xlarge"|"ml.c5.9xlarge"|"ml.c5.18xlarge"|"ml.m5.large"|"ml.m5.xlarge"|"ml.m5.2xlarge"|"ml.m5.4xlarge"|"ml.m5.12xlarge"|"ml.m5.24xlarge"|"ml.m6i.large"|"ml.m6i.xlarge"|"ml.m6i.2xlarge"|"ml.m6i.4xlarge"|"ml.m6i.8xlarge"|"ml.m6i.12xlarge"|"ml.m6i.16xlarge"|"ml.m6i.24xlarge"|"ml.m6i.32xlarge"|"ml.c6i.large"|"ml.c6i.xlarge"|"ml.c6i.2xlarge"|"ml.c6i.4xlarge"|"ml.c6i.8xlarge"|"ml.c6i.12xlarge"|"ml.c6i.16xlarge"|"ml.c6i.24xlarge"|"ml.c6i.32xlarge"|"ml.r6i.large"|"ml.r6i.xlarge"|"ml.r6i.2xlarge"|"ml.r6i.4xlarge"|"ml.r6i.8xlarge"|"ml.r6i.12xlarge"|"ml.r6i.16xlarge"|"ml.r6i.24xlarge"|"ml.r6i.32xlarge"|"ml.m7i.large"|"ml.m7i.xlarge"|"ml.m7i.2xlarge"|"ml.m7i.4xlarge"|"ml.m7i.8xlarge"|"ml.m7i.12xlarge"|"ml.m7i.16xlarge"|"ml.m7i.24xlarge"|"ml.m7i.48xlarge"|"ml.c7i.large"|"ml.c7i.xlarge"|"ml.c7i.2xlarge"|"ml.c7i.4xlarge"|"ml.c7i.8xlarge"|"ml.c7i.12xlarge"|"ml.c7i.16xlarge"|"ml.c7i.24xlarge"|"ml.c7i.48xlarge"|"ml.r7i.large"|"ml.r7i.xlarge"|"ml.r7i.2xlarge"|"ml.r7i.4xlarge"|"ml.r7i.8xlarge"|"ml.r7i.12xlarge"|"ml.r7i.16xlarge"|"ml.r7i.24xlarge"|"ml.r7i.48xlarge"|"ml.g4dn.xlarge"|"ml.g4dn.2xlarge"|"ml.g4dn.4xlarge"|"ml.g4dn.8xlarge"|"ml.g4dn.12xlarge"|"ml.g4dn.16xlarge"|"ml.g5.xlarge"|"ml.g5.2xlarge"|"ml.g5.4xlarge"|"ml.g5.8xlarge"|"ml.g5.12xlarge"|"ml.g5.16xlarge"|"ml.g5.24xlarge"|"ml.g5.48xlarge"|string;
  export type TransformInstanceTypes = TransformInstanceType[];
  export interface TransformJob {
    /**
     * The name of the transform job.
     */
    TransformJobName?: TransformJobName;
    /**
     * The Amazon Resource Name (ARN) of the transform job.
     */
    TransformJobArn?: TransformJobArn;
    /**
     * The status of the transform job. Transform job statuses are:    InProgress - The job is in progress.    Completed - The job has completed.    Failed - The transform job has failed. To see the reason for the failure, see the FailureReason field in the response to a DescribeTransformJob call.    Stopping - The transform job is stopping.    Stopped - The transform job has stopped.  
     */
    TransformJobStatus?: TransformJobStatus;
    /**
     * If the transform job failed, the reason it failed.
     */
    FailureReason?: FailureReason;
    /**
     * The name of the model associated with the transform job.
     */
    ModelName?: ModelName;
    /**
     * The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, SageMaker checks the optional execution-parameters to determine the settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For built-in algorithms, you don't need to set a value for MaxConcurrentTransforms.
     */
    MaxConcurrentTransforms?: MaxConcurrentTransforms;
    ModelClientConfig?: ModelClientConfig;
    /**
     * The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB. For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, SageMaker built-in algorithms do not support HTTP chunked encoding.
     */
    MaxPayloadInMB?: MaxPayloadInMB;
    /**
     * Specifies the number of records to include in a mini-batch for an HTTP inference request. A record is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.
     */
    BatchStrategy?: BatchStrategy;
    /**
     * The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
     */
    Environment?: TransformEnvironmentMap;
    TransformInput?: TransformInput;
    TransformOutput?: TransformOutput;
    DataCaptureConfig?: BatchDataCaptureConfig;
    TransformResources?: TransformResources;
    /**
     * A timestamp that shows when the transform Job was created.
     */
    CreationTime?: Timestamp;
    /**
     * Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.
     */
    TransformStartTime?: Timestamp;
    /**
     * Indicates when the transform job has been completed, or has stopped or failed. You are billed for the time interval between this time and the value of TransformStartTime.
     */
    TransformEndTime?: Timestamp;
    /**
     * The Amazon Resource Name (ARN) of the labeling job that created the transform job.
     */
    LabelingJobArn?: LabelingJobArn;
    /**
     * The Amazon Resource Name (ARN) of the AutoML job that created the transform job.
     */
    AutoMLJobArn?: AutoMLJobArn;
    DataProcessing?: DataProcessing;
    ExperimentConfig?: ExperimentConfig;
    /**
     * A list of tags associated with the transform job.
     */
    Tags?: TagList;
  }
  export type TransformJobArn = string;
  export interface TransformJobDefinition {
    /**
     * The maximum number of parallel requests that can be sent to each instance in a transform job. The default value is 1.
     */
    MaxConcurrentTransforms?: MaxConcurrentTransforms;
    /**
     * The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata).
     */
    MaxPayloadInMB?: MaxPayloadInMB;
    /**
     * A string that determines the number of records included in a single mini-batch.  SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.
     */
    BatchStrategy?: BatchStrategy;
    /**
     * The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.
     */
    Environment?: TransformEnvironmentMap;
    /**
     * A description of the input source and the way the transform job consumes it.
     */
    TransformInput: TransformInput;
    /**
     * Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
     */
    TransformOutput: TransformOutput;
    /**
     * Identifies the ML compute instances for the transform job.
     */
    TransformResources: TransformResources;
  }
  export type TransformJobName = string;
  export type TransformJobStatus = "InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export interface TransformJobStepMetadata {
    /**
     * The Amazon Resource Name (ARN) of the transform job that was run by this step execution.
     */
    Arn?: TransformJobArn;
  }
  export type TransformJobSummaries = TransformJobSummary[];
  export interface TransformJobSummary {
    /**
     * The name of the transform job.
     */
    TransformJobName: TransformJobName;
    /**
     * The Amazon Resource Name (ARN) of the transform job.
     */
    TransformJobArn: TransformJobArn;
    /**
     * A timestamp that shows when the transform Job was created.
     */
    CreationTime: Timestamp;
    /**
     * Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.
     */
    TransformEndTime?: Timestamp;
    /**
     * Indicates when the transform job was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * The status of the transform job.
     */
    TransformJobStatus: TransformJobStatus;
    /**
     * If the transform job failed, the reason it failed.
     */
    FailureReason?: FailureReason;
  }
  export interface TransformOutput {
    /**
     * The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix. For every S3 object used as input for the transform job, batch transform stores the transformed data with an .out suffix in a corresponding subfolder in the location in the output prefix. For example, for the input data stored at s3://bucket-name/input-name-prefix/dataset01/data.csv, batch transform stores the transformed data at s3://bucket-name/output-name-prefix/input-name-prefix/data.csv.out. Batch transform doesn't upload partially processed objects. For an input S3 object that contains multiple records, it creates an .out file only if the transform job succeeds on the entire file. When the input contains multiple S3 objects, the batch transform job processes the listed S3 objects and uploads only the output for successfully processed objects. If any object fails in the transform job batch transform marks the job as failed to prompt investigation.
     */
    S3OutputPath: S3Uri;
    /**
     * The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.
     */
    Accept?: Accept;
    /**
     * Defines how to assemble the results of the transform job as a single S3 object. Choose a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line.
     */
    AssembleWith?: AssemblyType;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption. The KmsKeyId can be any of the following formats:    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias    If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.  The KMS key policy must grant permission to the IAM role that you specify in your CreateModel request. For more information, see Using Key Policies in Amazon Web Services KMS in the Amazon Web Services Key Management Service Developer Guide.
     */
    KmsKeyId?: KmsKeyId;
  }
  export interface TransformResources {
    /**
     * The ML compute instance type for the transform job. If you are using built-in algorithms to transform moderately sized datasets, we recommend using ml.m4.xlarge or ml.m5.largeinstance types.
     */
    InstanceType: TransformInstanceType;
    /**
     * The number of ML compute instances to use in the transform job. The default value is 1, and the maximum is 100. For distributed transform jobs, specify a value greater than 1.
     */
    InstanceCount: TransformInstanceCount;
    /**
     * The Amazon Web Services Key Management Service (Amazon Web Services KMS) key that Amazon SageMaker uses to encrypt model data on the storage volume attached to the ML compute instance(s) that run the batch transform job.  Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a VolumeKmsKeyId when using an instance type with local storage. For a list of instance types that support local instance storage, see Instance Store Volumes. For more information about local instance storage encryption, see SSD Instance Store Volumes.   The VolumeKmsKeyId can be any of the following formats:   Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab    Alias name: alias/ExampleAlias    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias   
     */
    VolumeKmsKeyId?: KmsKeyId;
  }
  export interface TransformS3DataSource {
    /**
     * If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.  If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.  The following values are compatible: ManifestFile, S3Prefix  The following value is not compatible: AugmentedManifestFile 
     */
    S3DataType: S3DataType;
    /**
     * Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:    A key name prefix might look like this: s3://bucketname/exampleprefix/.     A manifest might look like this: s3://bucketname/example.manifest   The manifest is an S3 object which is a JSON file with the following format:   [ {"prefix": "s3://customer_bucket/some/prefix/"},   "relative/path/to/custdata-1",   "relative/path/custdata-2",   ...   "relative/path/custdata-N"   ]   The preceding JSON matches the following S3Uris:   s3://customer_bucket/some/prefix/relative/path/to/custdata-1   s3://customer_bucket/some/prefix/relative/path/custdata-2   ...   s3://customer_bucket/some/prefix/relative/path/custdata-N   The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.  
     */
    S3Uri: S3Uri;
  }
  export type TransformationAttributeName = string;
  export interface Trial {
    /**
     * The name of the trial.
     */
    TrialName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
    /**
     * The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The name of the experiment the trial is part of.
     */
    ExperimentName?: ExperimentEntityName;
    Source?: TrialSource;
    /**
     * When the trial was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the trial.
     */
    CreatedBy?: UserContext;
    /**
     * Who last modified the trial.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    MetadataProperties?: MetadataProperties;
    /**
     * The list of tags that are associated with the trial. You can use Search API to search on the tags.
     */
    Tags?: TagList;
    /**
     * A list of the components associated with the trial. For each component, a summary of the component's properties is included.
     */
    TrialComponentSummaries?: TrialComponentSimpleSummaries;
  }
  export type TrialArn = string;
  export interface TrialComponent {
    /**
     * The name of the trial component.
     */
    TrialComponentName?: ExperimentEntityName;
    /**
     * The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    /**
     * The Amazon Resource Name (ARN) and job type of the source of the component.
     */
    Source?: TrialComponentSource;
    Status?: TrialComponentStatus;
    /**
     * When the component started.
     */
    StartTime?: Timestamp;
    /**
     * When the component ended.
     */
    EndTime?: Timestamp;
    /**
     * When the component was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the trial component.
     */
    CreatedBy?: UserContext;
    /**
     * When the component was last modified.
     */
    LastModifiedTime?: Timestamp;
    LastModifiedBy?: UserContext;
    /**
     * The hyperparameters of the component.
     */
    Parameters?: TrialComponentParameters;
    /**
     * The input artifacts of the component.
     */
    InputArtifacts?: TrialComponentArtifacts;
    /**
     * The output artifacts of the component.
     */
    OutputArtifacts?: TrialComponentArtifacts;
    /**
     * The metrics for the component.
     */
    Metrics?: TrialComponentMetricSummaries;
    MetadataProperties?: MetadataProperties;
    /**
     * Details of the source of the component.
     */
    SourceDetail?: TrialComponentSourceDetail;
    /**
     * The Amazon Resource Name (ARN) of the lineage group resource.
     */
    LineageGroupArn?: LineageGroupArn;
    /**
     * The list of tags that are associated with the component. You can use Search API to search on the tags.
     */
    Tags?: TagList;
    /**
     * An array of the parents of the component. A parent is a trial the component is associated with and the experiment the trial is part of. A component might not have any parents.
     */
    Parents?: Parents;
    /**
     * The name of the experiment run.
     */
    RunName?: ExperimentEntityName;
  }
  export type TrialComponentArn = string;
  export interface TrialComponentArtifact {
    /**
     * The media type of the artifact, which indicates the type of data in the artifact file. The media type consists of a type and a subtype concatenated with a slash (/) character, for example, text/csv, image/jpeg, and s3/uri. The type specifies the category of the media. The subtype specifies the kind of data.
     */
    MediaType?: MediaType;
    /**
     * The location of the artifact.
     */
    Value: TrialComponentArtifactValue;
  }
  export type TrialComponentArtifactValue = string;
  export type TrialComponentArtifacts = {[key: string]: TrialComponentArtifact};
  export type TrialComponentKey128 = string;
  export type TrialComponentKey256 = string;
  export type TrialComponentKey320 = string;
  export type TrialComponentMetricSummaries = TrialComponentMetricSummary[];
  export interface TrialComponentMetricSummary {
    /**
     * The name of the metric.
     */
    MetricName?: MetricName;
    /**
     * The Amazon Resource Name (ARN) of the source.
     */
    SourceArn?: TrialComponentSourceArn;
    /**
     * When the metric was last updated.
     */
    TimeStamp?: Timestamp;
    /**
     * The maximum value of the metric.
     */
    Max?: OptionalDouble;
    /**
     * The minimum value of the metric.
     */
    Min?: OptionalDouble;
    /**
     * The most recent value of the metric.
     */
    Last?: OptionalDouble;
    /**
     * The number of samples used to generate the metric.
     */
    Count?: OptionalInteger;
    /**
     * The average value of the metric.
     */
    Avg?: OptionalDouble;
    /**
     * The standard deviation of the metric.
     */
    StdDev?: OptionalDouble;
  }
  export interface TrialComponentParameterValue {
    /**
     * The string value of a categorical hyperparameter. If you specify a value for this parameter, you can't specify the NumberValue parameter.
     */
    StringValue?: StringParameterValue;
    /**
     * The numeric value of a numeric hyperparameter. If you specify a value for this parameter, you can't specify the StringValue parameter.
     */
    NumberValue?: DoubleParameterValue;
  }
  export type TrialComponentParameters = {[key: string]: TrialComponentParameterValue};
  export type TrialComponentPrimaryStatus = "InProgress"|"Completed"|"Failed"|"Stopping"|"Stopped"|string;
  export type TrialComponentSimpleSummaries = TrialComponentSimpleSummary[];
  export interface TrialComponentSimpleSummary {
    /**
     * The name of the trial component.
     */
    TrialComponentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    TrialComponentSource?: TrialComponentSource;
    /**
     * When the component was created.
     */
    CreationTime?: Timestamp;
    CreatedBy?: UserContext;
  }
  export interface TrialComponentSource {
    /**
     * The source Amazon Resource Name (ARN).
     */
    SourceArn: TrialComponentSourceArn;
    /**
     * The source job type.
     */
    SourceType?: SourceType;
  }
  export type TrialComponentSourceArn = string;
  export interface TrialComponentSourceDetail {
    /**
     * The Amazon Resource Name (ARN) of the source.
     */
    SourceArn?: TrialComponentSourceArn;
    /**
     * Information about a training job that's the source of a trial component.
     */
    TrainingJob?: TrainingJob;
    /**
     * Information about a processing job that's the source of a trial component.
     */
    ProcessingJob?: ProcessingJob;
    /**
     * Information about a transform job that's the source of a trial component.
     */
    TransformJob?: TransformJob;
  }
  export type TrialComponentSources = TrialComponentSource[];
  export interface TrialComponentStatus {
    /**
     * The status of the trial component.
     */
    PrimaryStatus?: TrialComponentPrimaryStatus;
    /**
     * If the component failed, a message describing why.
     */
    Message?: TrialComponentStatusMessage;
  }
  export type TrialComponentStatusMessage = string;
  export type TrialComponentSummaries = TrialComponentSummary[];
  export interface TrialComponentSummary {
    /**
     * The name of the trial component.
     */
    TrialComponentName?: ExperimentEntityName;
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
    /**
     * The name of the component as displayed. If DisplayName isn't specified, TrialComponentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    TrialComponentSource?: TrialComponentSource;
    /**
     * The status of the component. States include:   InProgress   Completed   Failed  
     */
    Status?: TrialComponentStatus;
    /**
     * When the component started.
     */
    StartTime?: Timestamp;
    /**
     * When the component ended.
     */
    EndTime?: Timestamp;
    /**
     * When the component was created.
     */
    CreationTime?: Timestamp;
    /**
     * Who created the trial component.
     */
    CreatedBy?: UserContext;
    /**
     * When the component was last modified.
     */
    LastModifiedTime?: Timestamp;
    /**
     * Who last modified the component.
     */
    LastModifiedBy?: UserContext;
  }
  export interface TrialSource {
    /**
     * The Amazon Resource Name (ARN) of the source.
     */
    SourceArn: TrialSourceArn;
    /**
     * The source job type.
     */
    SourceType?: SourceType;
  }
  export type TrialSourceArn = string;
  export type TrialSummaries = TrialSummary[];
  export interface TrialSummary {
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
    /**
     * The name of the trial.
     */
    TrialName?: ExperimentEntityName;
    /**
     * The name of the trial as displayed. If DisplayName isn't specified, TrialName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    TrialSource?: TrialSource;
    /**
     * When the trial was created.
     */
    CreationTime?: Timestamp;
    /**
     * When the trial was last modified.
     */
    LastModifiedTime?: Timestamp;
  }
  export interface TtlDuration {
    /**
     *  TtlDuration time unit.
     */
    Unit?: TtlDurationUnit;
    /**
     *  TtlDuration time value.
     */
    Value?: TtlDurationValue;
  }
  export type TtlDurationUnit = "Seconds"|"Minutes"|"Hours"|"Days"|"Weeks"|string;
  export type TtlDurationValue = number;
  export interface TuningJobCompletionCriteria {
    /**
     * The value of the objective metric.
     */
    TargetObjectiveMetricValue?: TargetObjectiveMetricValue;
    /**
     * A flag to stop your hyperparameter tuning job if model performance fails to improve as evaluated against an objective function.
     */
    BestObjectiveNotImproving?: BestObjectiveNotImproving;
    /**
     * A flag to top your hyperparameter tuning job if automatic model tuning (AMT) has detected that your model has converged as evaluated against your objective function.
     */
    ConvergenceDetected?: ConvergenceDetected;
  }
  export interface TuningJobStepMetaData {
    /**
     * The Amazon Resource Name (ARN) of the tuning job that was run by this step execution.
     */
    Arn?: HyperParameterTuningJobArn;
  }
  export interface USD {
    /**
     * The whole number of dollars in the amount.
     */
    Dollars?: Dollars;
    /**
     * The fractional portion, in cents, of the amount. 
     */
    Cents?: Cents;
    /**
     * Fractions of a cent, in tenths.
     */
    TenthFractionsOfACent?: TenthFractionsOfACent;
  }
  export interface UiConfig {
    /**
     * The Amazon S3 bucket location of the UI template, or worker task template. This is the template used to render the worker UI and tools for labeling job tasks. For more information about the contents of a UI template, see  Creating Your Custom Labeling Task Template.
     */
    UiTemplateS3Uri?: S3Uri;
    /**
     * The ARN of the worker task template used to render the worker UI and tools for labeling job tasks. Use this parameter when you are creating a labeling job for named entity recognition, 3D point cloud and video frame labeling jobs. Use your labeling job task type to select one of the following ARNs and use it with this parameter when you create a labeling job. Replace aws-region with the Amazon Web Services Region you are creating your labeling job in. For example, replace aws-region with us-west-1 if you create a labeling job in US West (N. California).  Named Entity Recognition  Use the following HumanTaskUiArn for named entity recognition labeling jobs:  arn:aws:sagemaker:aws-region:394669845002:human-task-ui/NamedEntityRecognition   3D Point Cloud HumanTaskUiArns  Use this HumanTaskUiArn for 3D point cloud object detection and 3D point cloud object detection adjustment labeling jobs.     arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectDetection     Use this HumanTaskUiArn for 3D point cloud object tracking and 3D point cloud object tracking adjustment labeling jobs.     arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudObjectTracking     Use this HumanTaskUiArn for 3D point cloud semantic segmentation and 3D point cloud semantic segmentation adjustment labeling jobs.    arn:aws:sagemaker:aws-region:394669845002:human-task-ui/PointCloudSemanticSegmentation     Video Frame HumanTaskUiArns  Use this HumanTaskUiArn for video frame object detection and video frame object detection adjustment labeling jobs.     arn:aws:sagemaker:region:394669845002:human-task-ui/VideoObjectDetection     Use this HumanTaskUiArn for video frame object tracking and video frame object tracking adjustment labeling jobs.     arn:aws:sagemaker:aws-region:394669845002:human-task-ui/VideoObjectTracking   
     */
    HumanTaskUiArn?: HumanTaskUiArn;
  }
  export interface UiTemplate {
    /**
     * The content of the Liquid template for the worker user interface.
     */
    Content: TemplateContent;
  }
  export interface UiTemplateInfo {
    /**
     * The URL for the user interface template.
     */
    Url?: TemplateUrl;
    /**
     * The SHA-256 digest of the contents of the template.
     */
    ContentSha256?: TemplateContentSha256;
  }
  export type Uid = number;
  export interface UpdateActionRequest {
    /**
     * The name of the action to update.
     */
    ActionName: ExperimentEntityName;
    /**
     * The new description for the action.
     */
    Description?: ExperimentDescription;
    /**
     * The new status for the action.
     */
    Status?: ActionStatus;
    /**
     * The new list of properties. Overwrites the current property list.
     */
    Properties?: LineageEntityParameters;
    /**
     * A list of properties to remove.
     */
    PropertiesToRemove?: ListLineageEntityParameterKey;
  }
  export interface UpdateActionResponse {
    /**
     * The Amazon Resource Name (ARN) of the action.
     */
    ActionArn?: ActionArn;
  }
  export interface UpdateAppImageConfigRequest {
    /**
     * The name of the AppImageConfig to update.
     */
    AppImageConfigName: AppImageConfigName;
    /**
     * The new KernelGateway app to run on the image.
     */
    KernelGatewayImageConfig?: KernelGatewayImageConfig;
    /**
     * The JupyterLab app running on the image.
     */
    JupyterLabAppImageConfig?: JupyterLabAppImageConfig;
    /**
     * The Code Editor app running on the image.
     */
    CodeEditorAppImageConfig?: CodeEditorAppImageConfig;
  }
  export interface UpdateAppImageConfigResponse {
    /**
     * The ARN for the AppImageConfig.
     */
    AppImageConfigArn?: AppImageConfigArn;
  }
  export interface UpdateArtifactRequest {
    /**
     * The Amazon Resource Name (ARN) of the artifact to update.
     */
    ArtifactArn: ArtifactArn;
    /**
     * The new name for the artifact.
     */
    ArtifactName?: ExperimentEntityName;
    /**
     * The new list of properties. Overwrites the current property list.
     */
    Properties?: ArtifactProperties;
    /**
     * A list of properties to remove.
     */
    PropertiesToRemove?: ListLineageEntityParameterKey;
  }
  export interface UpdateArtifactResponse {
    /**
     * The Amazon Resource Name (ARN) of the artifact.
     */
    ArtifactArn?: ArtifactArn;
  }
  export interface UpdateClusterRequest {
    /**
     * Specify the name of the SageMaker HyperPod cluster you want to update.
     */
    ClusterName: ClusterNameOrArn;
    /**
     * Specify the instance groups to update.
     */
    InstanceGroups: ClusterInstanceGroupSpecifications;
  }
  export interface UpdateClusterResponse {
    /**
     * The Amazon Resource Name (ARN) of the updated SageMaker HyperPod cluster.
     */
    ClusterArn: ClusterArn;
  }
  export interface UpdateClusterSoftwareRequest {
    /**
     * Specify the name or the Amazon Resource Name (ARN) of the SageMaker HyperPod cluster you want to update for security patching.
     */
    ClusterName: ClusterNameOrArn;
  }
  export interface UpdateClusterSoftwareResponse {
    /**
     * The Amazon Resource Name (ARN) of the SageMaker HyperPod cluster being updated for security patching.
     */
    ClusterArn: ClusterArn;
  }
  export interface UpdateCodeRepositoryInput {
    /**
     * The name of the Git repository to update.
     */
    CodeRepositoryName: EntityName;
    /**
     * The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the Amazon Web Services Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:  {"username": UserName, "password": Password} 
     */
    GitConfig?: GitConfigForUpdate;
  }
  export interface UpdateCodeRepositoryOutput {
    /**
     * The ARN of the Git repository.
     */
    CodeRepositoryArn: CodeRepositoryArn;
  }
  export interface UpdateContextRequest {
    /**
     * The name of the context to update.
     */
    ContextName: ContextName;
    /**
     * The new description for the context.
     */
    Description?: ExperimentDescription;
    /**
     * The new list of properties. Overwrites the current property list.
     */
    Properties?: LineageEntityParameters;
    /**
     * A list of properties to remove.
     */
    PropertiesToRemove?: ListLineageEntityParameterKey;
  }
  export interface UpdateContextResponse {
    /**
     * The Amazon Resource Name (ARN) of the context.
     */
    ContextArn?: ContextArn;
  }
  export interface UpdateDeviceFleetRequest {
    /**
     * The name of the fleet.
     */
    DeviceFleetName: EntityName;
    /**
     * The Amazon Resource Name (ARN) of the device.
     */
    RoleArn?: RoleArn;
    /**
     * Description of the fleet.
     */
    Description?: DeviceFleetDescription;
    /**
     * Output configuration for storing sample data collected by the fleet.
     */
    OutputConfig: EdgeOutputConfig;
    /**
     * Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-{DeviceFleetName}". For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".
     */
    EnableIotRoleAlias?: EnableIotRoleAlias;
  }
  export interface UpdateDevicesRequest {
    /**
     * The name of the fleet the devices belong to.
     */
    DeviceFleetName: EntityName;
    /**
     * List of devices to register with Edge Manager agent.
     */
    Devices: Devices;
  }
  export interface UpdateDomainRequest {
    /**
     * The ID of the domain to be updated.
     */
    DomainId: DomainId;
    /**
     * A collection of settings.
     */
    DefaultUserSettings?: UserSettings;
    /**
     * A collection of DomainSettings configuration values to update.
     */
    DomainSettingsForUpdate?: DomainSettingsForUpdate;
    /**
     * The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.
     */
    AppSecurityGroupManagement?: AppSecurityGroupManagement;
    /**
     * The default settings used to create a space within the domain.
     */
    DefaultSpaceSettings?: DefaultSpaceSettings;
    /**
     * The VPC subnets that Studio uses for communication. If removing subnets, ensure there are no apps in the InService, Pending, or Deleting state.
     */
    SubnetIds?: Subnets;
    /**
     * Specifies the VPC used for non-EFS traffic.    PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker, which allows direct internet access.    VpcOnly - All Studio traffic is through the specified VPC and subnets.   This configuration can only be modified if there are no apps in the InService, Pending, or Deleting state. The configuration cannot be updated if DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is already set or DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided as part of the same request.
     */
    AppNetworkAccessType?: AppNetworkAccessType;
  }
  export interface UpdateDomainResponse {
    /**
     * The Amazon Resource Name (ARN) of the domain.
     */
    DomainArn?: DomainArn;
  }
  export interface UpdateEndpointInput {
    /**
     * The name of the endpoint whose configuration you want to update.
     */
    EndpointName: EndpointName;
    /**
     * The name of the new endpoint configuration.
     */
    EndpointConfigName: EndpointConfigName;
    /**
     * When updating endpoint resources, enables or disables the retention of variant properties, such as the instance count or the variant weight. To retain the variant properties of an endpoint when updating it, set RetainAllVariantProperties to true. To use the variant properties specified in a new EndpointConfig call when updating an endpoint, set RetainAllVariantProperties to false. The default is false.
     */
    RetainAllVariantProperties?: Boolean;
    /**
     * When you are updating endpoint resources with RetainAllVariantProperties, whose value is set to true, ExcludeRetainedVariantProperties specifies the list of type VariantProperty to override with the values provided by EndpointConfig. If you don't specify a value for ExcludeRetainedVariantProperties, no variant properties are overridden. 
     */
    ExcludeRetainedVariantProperties?: VariantPropertyList;
    /**
     * The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.
     */
    DeploymentConfig?: DeploymentConfig;
    /**
     * Specifies whether to reuse the last deployment configuration. The default value is false (the configuration is not reused).
     */
    RetainDeploymentConfig?: Boolean;
  }
  export interface UpdateEndpointOutput {
    /**
     * The Amazon Resource Name (ARN) of the endpoint.
     */
    EndpointArn: EndpointArn;
  }
  export interface UpdateEndpointWeightsAndCapacitiesInput {
    /**
     * The name of an existing SageMaker endpoint.
     */
    EndpointName: EndpointName;
    /**
     * An object that provides new capacity and weight values for a variant.
     */
    DesiredWeightsAndCapacities: DesiredWeightAndCapacityList;
  }
  export interface UpdateEndpointWeightsAndCapacitiesOutput {
    /**
     * The Amazon Resource Name (ARN) of the updated endpoint.
     */
    EndpointArn: EndpointArn;
  }
  export interface UpdateExperimentRequest {
    /**
     * The name of the experiment to update.
     */
    ExperimentName: ExperimentEntityName;
    /**
     * The name of the experiment as displayed. The name doesn't need to be unique. If DisplayName isn't specified, ExperimentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The description of the experiment.
     */
    Description?: ExperimentDescription;
  }
  export interface UpdateExperimentResponse {
    /**
     * The Amazon Resource Name (ARN) of the experiment.
     */
    ExperimentArn?: ExperimentArn;
  }
  export interface UpdateFeatureGroupRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the feature group that you're updating.
     */
    FeatureGroupName: FeatureGroupNameOrArn;
    /**
     * Updates the feature group. Updating a feature group is an asynchronous operation. When you get an HTTP 200 response, you've made a valid request. It takes some time after you've made a valid request for Feature Store to update the feature group.
     */
    FeatureAdditions?: FeatureAdditions;
    /**
     * Updates the feature group online store configuration.
     */
    OnlineStoreConfig?: OnlineStoreConfigUpdate;
    ThroughputConfig?: ThroughputConfigUpdate;
  }
  export interface UpdateFeatureGroupResponse {
    /**
     * The Amazon Resource Number (ARN) of the feature group that you're updating.
     */
    FeatureGroupArn: FeatureGroupArn;
  }
  export interface UpdateFeatureMetadataRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the feature group containing the feature that you're updating.
     */
    FeatureGroupName: FeatureGroupNameOrArn;
    /**
     * The name of the feature that you're updating.
     */
    FeatureName: FeatureName;
    /**
     * A description that you can write to better describe the feature.
     */
    Description?: FeatureDescription;
    /**
     * A list of key-value pairs that you can add to better describe the feature.
     */
    ParameterAdditions?: FeatureParameterAdditions;
    /**
     * A list of parameter keys that you can specify to remove parameters that describe your feature.
     */
    ParameterRemovals?: FeatureParameterRemovals;
  }
  export interface UpdateHubRequest {
    /**
     * The name of the hub to update.
     */
    HubName: HubNameOrArn;
    /**
     * A description of the updated hub.
     */
    HubDescription?: HubDescription;
    /**
     * The display name of the hub.
     */
    HubDisplayName?: HubDisplayName;
    /**
     * The searchable keywords for the hub.
     */
    HubSearchKeywords?: HubSearchKeywordList;
  }
  export interface UpdateHubResponse {
    /**
     * The Amazon Resource Name (ARN) of the updated hub.
     */
    HubArn: HubArn;
  }
  export interface UpdateImageRequest {
    /**
     * A list of properties to delete. Only the Description and DisplayName properties can be deleted.
     */
    DeleteProperties?: ImageDeletePropertyList;
    /**
     * The new description for the image.
     */
    Description?: ImageDescription;
    /**
     * The new display name for the image.
     */
    DisplayName?: ImageDisplayName;
    /**
     * The name of the image to update.
     */
    ImageName: ImageName;
    /**
     * The new ARN for the IAM role that enables Amazon SageMaker to perform tasks on your behalf.
     */
    RoleArn?: RoleArn;
  }
  export interface UpdateImageResponse {
    /**
     * The ARN of the image.
     */
    ImageArn?: ImageArn;
  }
  export interface UpdateImageVersionRequest {
    /**
     * The name of the image.
     */
    ImageName: ImageName;
    /**
     * The alias of the image version.
     */
    Alias?: SageMakerImageVersionAlias;
    /**
     * The version of the image.
     */
    Version?: ImageVersionNumber;
    /**
     * A list of aliases to add.
     */
    AliasesToAdd?: SageMakerImageVersionAliases;
    /**
     * A list of aliases to delete.
     */
    AliasesToDelete?: SageMakerImageVersionAliases;
    /**
     * The availability of the image version specified by the maintainer.    NOT_PROVIDED: The maintainers did not provide a status for image version stability.    STABLE: The image version is stable.    TO_BE_ARCHIVED: The image version is set to be archived. Custom image versions that are set to be archived are automatically archived after three months.    ARCHIVED: The image version is archived. Archived image versions are not searchable and are no longer actively supported.   
     */
    VendorGuidance?: VendorGuidance;
    /**
     * Indicates SageMaker job type compatibility.    TRAINING: The image version is compatible with SageMaker training jobs.    INFERENCE: The image version is compatible with SageMaker inference jobs.    NOTEBOOK_KERNEL: The image version is compatible with SageMaker notebook kernels.  
     */
    JobType?: JobType;
    /**
     * The machine learning framework vended in the image version.
     */
    MLFramework?: MLFramework;
    /**
     * The supported programming language and its version.
     */
    ProgrammingLang?: ProgrammingLang;
    /**
     * Indicates CPU or GPU compatibility.    CPU: The image version is compatible with CPU.    GPU: The image version is compatible with GPU.  
     */
    Processor?: Processor;
    /**
     * Indicates Horovod compatibility.
     */
    Horovod?: Horovod;
    /**
     * The maintainer description of the image version.
     */
    ReleaseNotes?: ReleaseNotes;
  }
  export interface UpdateImageVersionResponse {
    /**
     * The ARN of the image version.
     */
    ImageVersionArn?: ImageVersionArn;
  }
  export interface UpdateInferenceComponentInput {
    /**
     * The name of the inference component.
     */
    InferenceComponentName: InferenceComponentName;
    /**
     * Details about the resources to deploy with this inference component, including the model, container, and compute resources.
     */
    Specification?: InferenceComponentSpecification;
    /**
     * Runtime settings for a model that is deployed with an inference component.
     */
    RuntimeConfig?: InferenceComponentRuntimeConfig;
  }
  export interface UpdateInferenceComponentOutput {
    /**
     * The Amazon Resource Name (ARN) of the inference component.
     */
    InferenceComponentArn: InferenceComponentArn;
  }
  export interface UpdateInferenceComponentRuntimeConfigInput {
    /**
     * The name of the inference component to update.
     */
    InferenceComponentName: InferenceComponentName;
    /**
     * Runtime settings for a model that is deployed with an inference component.
     */
    DesiredRuntimeConfig: InferenceComponentRuntimeConfig;
  }
  export interface UpdateInferenceComponentRuntimeConfigOutput {
    /**
     * The Amazon Resource Name (ARN) of the inference component.
     */
    InferenceComponentArn: InferenceComponentArn;
  }
  export interface UpdateInferenceExperimentRequest {
    /**
     * The name of the inference experiment to be updated.
     */
    Name: InferenceExperimentName;
    /**
     *  The duration for which the inference experiment will run. If the status of the inference experiment is Created, then you can update both the start and end dates. If the status of the inference experiment is Running, then you can update only the end date. 
     */
    Schedule?: InferenceExperimentSchedule;
    /**
     * The description of the inference experiment.
     */
    Description?: InferenceExperimentDescription;
    /**
     *  An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure configuration you want to update. 
     */
    ModelVariants?: ModelVariantConfigList;
    /**
     * The Amazon S3 location and configuration for storing inference request and response data.
     */
    DataStorageConfig?: InferenceExperimentDataStorageConfig;
    /**
     *  The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates. 
     */
    ShadowModeConfig?: ShadowModeConfig;
  }
  export interface UpdateInferenceExperimentResponse {
    /**
     * The ARN of the updated inference experiment.
     */
    InferenceExperimentArn: InferenceExperimentArn;
  }
  export interface UpdateMlflowTrackingServerRequest {
    /**
     * The name of the MLflow Tracking Server to update.
     */
    TrackingServerName: TrackingServerName;
    /**
     * The new S3 URI for the general purpose bucket to use as the artifact store for the MLflow Tracking Server.
     */
    ArtifactStoreUri?: S3Uri;
    /**
     * The new size for the MLflow Tracking Server.
     */
    TrackingServerSize?: TrackingServerSize;
    /**
     * Whether to enable or disable automatic registration of new MLflow models to the SageMaker Model Registry. To enable automatic model registration, set this value to True. To disable automatic model registration, set this value to False. If not specified, AutomaticModelRegistration defaults to False 
     */
    AutomaticModelRegistration?: Boolean;
    /**
     * The new weekly maintenance window start day and time to update. The maintenance window day and time should be in Coordinated Universal Time (UTC) 24-hour standard time. For example: TUE:03:30.
     */
    WeeklyMaintenanceWindowStart?: WeeklyMaintenanceWindowStart;
  }
  export interface UpdateMlflowTrackingServerResponse {
    /**
     * The ARN of the updated MLflow Tracking Server.
     */
    TrackingServerArn?: TrackingServerArn;
  }
  export interface UpdateModelCardRequest {
    /**
     * The name or Amazon Resource Name (ARN) of the model card to update.
     */
    ModelCardName: ModelCardNameOrArn;
    /**
     * The updated model card content. Content must be in model card JSON schema and provided as a string. When updating model card content, be sure to include the full content and not just updated content.
     */
    Content?: ModelCardContent;
    /**
     * The approval status of the model card within your organization. Different organizations might have different criteria for model card review and approval.    Draft: The model card is a work in progress.    PendingReview: The model card is pending review.    Approved: The model card is approved.    Archived: The model card is archived. No more updates should be made to the model card, but it can still be exported.  
     */
    ModelCardStatus?: ModelCardStatus;
  }
  export interface UpdateModelCardResponse {
    /**
     * The Amazon Resource Name (ARN) of the updated model card.
     */
    ModelCardArn: ModelCardArn;
  }
  export interface UpdateModelPackageInput {
    /**
     * The Amazon Resource Name (ARN) of the model package.
     */
    ModelPackageArn: ModelPackageArn;
    /**
     * The approval status of the model.
     */
    ModelApprovalStatus?: ModelApprovalStatus;
    /**
     * A description for the approval status of the model.
     */
    ApprovalDescription?: ApprovalDescription;
    /**
     * The metadata properties associated with the model package versions.
     */
    CustomerMetadataProperties?: CustomerMetadataMap;
    /**
     * The metadata properties associated with the model package versions to remove.
     */
    CustomerMetadataPropertiesToRemove?: CustomerMetadataKeyList;
    /**
     * An array of additional Inference Specification objects to be added to the existing array additional Inference Specification. Total number of additional Inference Specifications can not exceed 15. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.
     */
    AdditionalInferenceSpecificationsToAdd?: AdditionalInferenceSpecifications;
    /**
     * Specifies details about inference jobs that you can run with models based on this model package, including the following information:   The Amazon ECR paths of containers that contain the inference code and model artifacts.   The instance types that the model package supports for transform jobs and real-time endpoints used for inference.   The input and output content formats that the model package supports for inference.  
     */
    InferenceSpecification?: InferenceSpecification;
    /**
     * The URI of the source for the model package.
     */
    SourceUri?: ModelPackageSourceUri;
    /**
     * The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.
     */
    ModelCard?: ModelPackageModelCard;
  }
  export interface UpdateModelPackageOutput {
    /**
     * The Amazon Resource Name (ARN) of the model.
     */
    ModelPackageArn: ModelPackageArn;
  }
  export interface UpdateMonitoringAlertRequest {
    /**
     * The name of a monitoring schedule.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The name of a monitoring alert.
     */
    MonitoringAlertName: MonitoringAlertName;
    /**
     * Within EvaluationPeriod, how many execution failures will raise an alert.
     */
    DatapointsToAlert: MonitoringDatapointsToAlert;
    /**
     * The number of most recent monitoring executions to consider when evaluating alert status.
     */
    EvaluationPeriod: MonitoringEvaluationPeriod;
  }
  export interface UpdateMonitoringAlertResponse {
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn: MonitoringScheduleArn;
    /**
     * The name of a monitoring alert.
     */
    MonitoringAlertName?: MonitoringAlertName;
  }
  export interface UpdateMonitoringScheduleRequest {
    /**
     * The name of the monitoring schedule. The name must be unique within an Amazon Web Services Region within an Amazon Web Services account.
     */
    MonitoringScheduleName: MonitoringScheduleName;
    /**
     * The configuration object that specifies the monitoring schedule and defines the monitoring job.
     */
    MonitoringScheduleConfig: MonitoringScheduleConfig;
  }
  export interface UpdateMonitoringScheduleResponse {
    /**
     * The Amazon Resource Name (ARN) of the monitoring schedule.
     */
    MonitoringScheduleArn: MonitoringScheduleArn;
  }
  export interface UpdateNotebookInstanceInput {
    /**
     * The name of the notebook instance to update.
     */
    NotebookInstanceName: NotebookInstanceName;
    /**
     * The Amazon ML compute instance type.
     */
    InstanceType?: InstanceType;
    /**
     * The Amazon Resource Name (ARN) of the IAM role that SageMaker can assume to access the notebook instance. For more information, see SageMaker Roles.   To be able to pass this role to SageMaker, the caller of this API must have the iam:PassRole permission. 
     */
    RoleArn?: RoleArn;
    /**
     * The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.
     */
    LifecycleConfigName?: NotebookInstanceLifecycleConfigName;
    /**
     * Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.
     */
    DisassociateLifecycleConfig?: DisassociateNotebookInstanceLifecycleConfig;
    /**
     * The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so SageMaker can't determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can't decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.
     */
    VolumeSizeInGB?: NotebookInstanceVolumeSizeInGB;
    /**
     * The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in Amazon Web Services CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    DefaultCodeRepository?: CodeRepositoryNameOrUrl;
    /**
     * An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in Amazon Web Services CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with SageMaker Notebook Instances.
     */
    AdditionalCodeRepositories?: AdditionalCodeRepositoryNamesOrUrls;
    /**
     * A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.
     */
    AcceleratorTypes?: NotebookInstanceAcceleratorTypes;
    /**
     * A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.
     */
    DisassociateAcceleratorTypes?: DisassociateNotebookInstanceAcceleratorTypes;
    /**
     * The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
     */
    DisassociateDefaultCodeRepository?: DisassociateDefaultCodeRepository;
    /**
     * A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.
     */
    DisassociateAdditionalCodeRepositories?: DisassociateAdditionalCodeRepositories;
    /**
     * Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.  If you set this to Disabled, users don't have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions. 
     */
    RootAccess?: RootAccess;
    /**
     * Information on the IMDS configuration of the notebook instance
     */
    InstanceMetadataServiceConfiguration?: InstanceMetadataServiceConfiguration;
  }
  export interface UpdateNotebookInstanceLifecycleConfigInput {
    /**
     * The name of the lifecycle configuration.
     */
    NotebookInstanceLifecycleConfigName: NotebookInstanceLifecycleConfigName;
    /**
     * The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.
     */
    OnCreate?: NotebookInstanceLifecycleConfigList;
    /**
     * The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.
     */
    OnStart?: NotebookInstanceLifecycleConfigList;
  }
  export interface UpdateNotebookInstanceLifecycleConfigOutput {
  }
  export interface UpdateNotebookInstanceOutput {
  }
  export interface UpdatePipelineExecutionRequest {
    /**
     * The Amazon Resource Name (ARN) of the pipeline execution.
     */
    PipelineExecutionArn: PipelineExecutionArn;
    /**
     * The description of the pipeline execution.
     */
    PipelineExecutionDescription?: PipelineExecutionDescription;
    /**
     * The display name of the pipeline execution.
     */
    PipelineExecutionDisplayName?: PipelineExecutionName;
    /**
     * This configuration, if specified, overrides the parallelism configuration of the parent pipeline for this specific run.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
  }
  export interface UpdatePipelineExecutionResponse {
    /**
     * The Amazon Resource Name (ARN) of the updated pipeline execution.
     */
    PipelineExecutionArn?: PipelineExecutionArn;
  }
  export interface UpdatePipelineRequest {
    /**
     * The name of the pipeline to update.
     */
    PipelineName: PipelineName;
    /**
     * The display name of the pipeline.
     */
    PipelineDisplayName?: PipelineName;
    /**
     * The JSON pipeline definition.
     */
    PipelineDefinition?: PipelineDefinition;
    /**
     * The location of the pipeline definition stored in Amazon S3. If specified, SageMaker will retrieve the pipeline definition from this location.
     */
    PipelineDefinitionS3Location?: PipelineDefinitionS3Location;
    /**
     * The description of the pipeline.
     */
    PipelineDescription?: PipelineDescription;
    /**
     * The Amazon Resource Name (ARN) that the pipeline uses to execute.
     */
    RoleArn?: RoleArn;
    /**
     * If specified, it applies to all executions of this pipeline by default.
     */
    ParallelismConfiguration?: ParallelismConfiguration;
  }
  export interface UpdatePipelineResponse {
    /**
     * The Amazon Resource Name (ARN) of the updated pipeline.
     */
    PipelineArn?: PipelineArn;
  }
  export interface UpdateProjectInput {
    /**
     * The name of the project.
     */
    ProjectName: ProjectEntityName;
    /**
     * The description for the project.
     */
    ProjectDescription?: EntityDescription;
    /**
     * The product ID and provisioning artifact ID to provision a service catalog. The provisioning artifact ID will default to the latest provisioning artifact ID of the product, if you don't provide the provisioning artifact ID. For more information, see What is Amazon Web Services Service Catalog. 
     */
    ServiceCatalogProvisioningUpdateDetails?: ServiceCatalogProvisioningUpdateDetails;
    /**
     * An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources. In addition, the project must have tag update constraints set in order to include this parameter in the request. For more information, see Amazon Web Services Service Catalog Tag Update Constraints.
     */
    Tags?: TagList;
  }
  export interface UpdateProjectOutput {
    /**
     * The Amazon Resource Name (ARN) of the project.
     */
    ProjectArn: ProjectArn;
  }
  export interface UpdateSpaceRequest {
    /**
     * The ID of the associated domain.
     */
    DomainId: DomainId;
    /**
     * The name of the space.
     */
    SpaceName: SpaceName;
    /**
     * A collection of space settings.
     */
    SpaceSettings?: SpaceSettings;
    /**
     * The name of the space that appears in the Amazon SageMaker Studio UI.
     */
    SpaceDisplayName?: NonEmptyString64;
  }
  export interface UpdateSpaceResponse {
    /**
     * The space's Amazon Resource Name (ARN).
     */
    SpaceArn?: SpaceArn;
  }
  export interface UpdateTrainingJobRequest {
    /**
     * The name of a training job to update the Debugger profiling configuration.
     */
    TrainingJobName: TrainingJobName;
    /**
     * Configuration information for Amazon SageMaker Debugger system monitoring, framework profiling, and storage paths.
     */
    ProfilerConfig?: ProfilerConfigForUpdate;
    /**
     * Configuration information for Amazon SageMaker Debugger rules for profiling system and framework metrics.
     */
    ProfilerRuleConfigurations?: ProfilerRuleConfigurations;
    /**
     * The training job ResourceConfig to update warm pool retention length.
     */
    ResourceConfig?: ResourceConfigForUpdate;
    /**
     * Configuration for remote debugging while the training job is running. You can update the remote debugging configuration when the SecondaryStatus of the job is Downloading or Training.To learn more about the remote debugging functionality of SageMaker, see Access a training container through Amazon Web Services Systems Manager (SSM) for remote debugging.
     */
    RemoteDebugConfig?: RemoteDebugConfigForUpdate;
  }
  export interface UpdateTrainingJobResponse {
    /**
     * The Amazon Resource Name (ARN) of the training job.
     */
    TrainingJobArn: TrainingJobArn;
  }
  export interface UpdateTrialComponentRequest {
    /**
     * The name of the component to update.
     */
    TrialComponentName: ExperimentEntityName;
    /**
     * The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.
     */
    DisplayName?: ExperimentEntityName;
    /**
     * The new status of the component.
     */
    Status?: TrialComponentStatus;
    /**
     * When the component started.
     */
    StartTime?: Timestamp;
    /**
     * When the component ended.
     */
    EndTime?: Timestamp;
    /**
     * Replaces all of the component's hyperparameters with the specified hyperparameters or add new hyperparameters. Existing hyperparameters are replaced if the trial component is updated with an identical hyperparameter key.
     */
    Parameters?: TrialComponentParameters;
    /**
     * The hyperparameters to remove from the component.
     */
    ParametersToRemove?: ListTrialComponentKey256;
    /**
     * Replaces all of the component's input artifacts with the specified artifacts or adds new input artifacts. Existing input artifacts are replaced if the trial component is updated with an identical input artifact key.
     */
    InputArtifacts?: TrialComponentArtifacts;
    /**
     * The input artifacts to remove from the component.
     */
    InputArtifactsToRemove?: ListTrialComponentKey256;
    /**
     * Replaces all of the component's output artifacts with the specified artifacts or adds new output artifacts. Existing output artifacts are replaced if the trial component is updated with an identical output artifact key.
     */
    OutputArtifacts?: TrialComponentArtifacts;
    /**
     * The output artifacts to remove from the component.
     */
    OutputArtifactsToRemove?: ListTrialComponentKey256;
  }
  export interface UpdateTrialComponentResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial component.
     */
    TrialComponentArn?: TrialComponentArn;
  }
  export interface UpdateTrialRequest {
    /**
     * The name of the trial to update.
     */
    TrialName: ExperimentEntityName;
    /**
     * The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.
     */
    DisplayName?: ExperimentEntityName;
  }
  export interface UpdateTrialResponse {
    /**
     * The Amazon Resource Name (ARN) of the trial.
     */
    TrialArn?: TrialArn;
  }
  export interface UpdateUserProfileRequest {
    /**
     * The domain ID.
     */
    DomainId: DomainId;
    /**
     * The user profile name.
     */
    UserProfileName: UserProfileName;
    /**
     * A collection of settings.
     */
    UserSettings?: UserSettings;
  }
  export interface UpdateUserProfileResponse {
    /**
     * The user profile Amazon Resource Name (ARN).
     */
    UserProfileArn?: UserProfileArn;
  }
  export interface UpdateWorkforceRequest {
    /**
     * The name of the private workforce that you want to update. You can find your workforce name by using the ListWorkforces operation.
     */
    WorkforceName: WorkforceName;
    /**
     * A list of one to ten worker IP address ranges (CIDRs) that can be used to access tasks assigned to this workforce. Maximum: Ten CIDR values
     */
    SourceIpConfig?: SourceIpConfig;
    /**
     * Use this parameter to update your OIDC Identity Provider (IdP) configuration for a workforce made using your own IdP.
     */
    OidcConfig?: OidcConfig;
    /**
     * Use this parameter to update your VPC configuration for a workforce.
     */
    WorkforceVpcConfig?: WorkforceVpcConfigRequest;
  }
  export interface UpdateWorkforceResponse {
    /**
     * A single private workforce. You can create one private work force in each Amazon Web Services Region. By default, any workforce-related API operation used in a specific region will apply to the workforce created in that region. To learn how to create a private workforce, see Create a Private Workforce.
     */
    Workforce: Workforce;
  }
  export interface UpdateWorkteamRequest {
    /**
     * The name of the work team to update.
     */
    WorkteamName: WorkteamName;
    /**
     * A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.  Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition. You should not provide input for both of these parameters in a single request. For workforces created using Amazon Cognito, private work teams correspond to Amazon Cognito user groups within the user pool used to create a workforce. All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values. To add a Amazon Cognito user group to an existing worker pool, see Adding groups to a User Pool. For more information about user pools, see Amazon Cognito User Pools. For workforces created using your own OIDC IdP, specify the user groups that you want to include in your private work team in OidcMemberDefinition by listing those groups in Groups. Be aware that user groups that are already in the work team must also be listed in Groups when you make this request to remain on the work team. If you do not include these user groups, they will no longer be associated with the work team you update. 
     */
    MemberDefinitions?: MemberDefinitions;
    /**
     * An updated description for the work team.
     */
    Description?: String200;
    /**
     * Configures SNS topic notifications for available or expiring work items
     */
    NotificationConfiguration?: NotificationConfiguration;
    /**
     * Use this optional parameter to constrain access to an Amazon S3 resource based on the IP address using supported IAM global condition keys. The Amazon S3 resource is accessed in the worker portal using a Amazon S3 presigned URL.
     */
    WorkerAccessConfiguration?: WorkerAccessConfiguration;
  }
  export interface UpdateWorkteamResponse {
    /**
     * A Workteam object that describes the updated work team.
     */
    Workteam: Workteam;
  }
  export type Url = string;
  export interface UserContext {
    /**
     * The Amazon Resource Name (ARN) of the user's profile.
     */
    UserProfileArn?: String;
    /**
     * The name of the user's profile.
     */
    UserProfileName?: String;
    /**
     * The domain associated with the user.
     */
    DomainId?: String;
    /**
     * The IAM Identity details associated with the user. These details are associated with model package groups, model packages, and project entities only.
     */
    IamIdentity?: IamIdentity;
  }
  export type UserProfileArn = string;
  export interface UserProfileDetails {
    /**
     * The domain ID.
     */
    DomainId?: DomainId;
    /**
     * The user profile name.
     */
    UserProfileName?: UserProfileName;
    /**
     * The status.
     */
    Status?: UserProfileStatus;
    /**
     * The creation time.
     */
    CreationTime?: CreationTime;
    /**
     * The last modified time.
     */
    LastModifiedTime?: LastModifiedTime;
  }
  export type UserProfileList = UserProfileDetails[];
  export type UserProfileName = string;
  export type UserProfileSortKey = "CreationTime"|"LastModifiedTime"|string;
  export type UserProfileStatus = "Deleting"|"Failed"|"InService"|"Pending"|"Updating"|"Update_Failed"|"Delete_Failed"|string;
  export interface UserSettings {
    /**
     * The execution role for the user.
     */
    ExecutionRole?: RoleArn;
    /**
     * The security groups for the Amazon Virtual Private Cloud (VPC) that the domain uses for communication. Optional when the CreateDomain.AppNetworkAccessType parameter is set to PublicInternetOnly. Required when the CreateDomain.AppNetworkAccessType parameter is set to VpcOnly, unless specified as part of the DefaultUserSettings for the domain. Amazon SageMaker adds a security group to allow NFS traffic from Amazon SageMaker Studio. Therefore, the number of security groups that you can specify is one less than the maximum number shown.
     */
    SecurityGroups?: SecurityGroupIds;
    /**
     * Specifies options for sharing Amazon SageMaker Studio notebooks.
     */
    SharingSettings?: SharingSettings;
    /**
     * The Jupyter server's app settings.
     */
    JupyterServerAppSettings?: JupyterServerAppSettings;
    /**
     * The kernel gateway app settings.
     */
    KernelGatewayAppSettings?: KernelGatewayAppSettings;
    /**
     * The TensorBoard app settings.
     */
    TensorBoardAppSettings?: TensorBoardAppSettings;
    /**
     * A collection of settings that configure user interaction with the RStudioServerPro app.
     */
    RStudioServerProAppSettings?: RStudioServerProAppSettings;
    /**
     * A collection of settings that configure the RSessionGateway app.
     */
    RSessionAppSettings?: RSessionAppSettings;
    /**
     * The Canvas app settings.
     */
    CanvasAppSettings?: CanvasAppSettings;
    /**
     * The Code Editor application settings.
     */
    CodeEditorAppSettings?: CodeEditorAppSettings;
    /**
     * The settings for the JupyterLab application.
     */
    JupyterLabAppSettings?: JupyterLabAppSettings;
    /**
     * The storage settings for a space.
     */
    SpaceStorageSettings?: DefaultSpaceStorageSettings;
    /**
     * The default experience that the user is directed to when accessing the domain. The supported values are:    studio::: Indicates that Studio is the default experience. This value can only be passed if StudioWebPortal is set to ENABLED.    app:JupyterServer:: Indicates that Studio Classic is the default experience.  
     */
    DefaultLandingUri?: LandingUri;
    /**
     * Whether the user can access Studio. If this value is set to DISABLED, the user cannot access Studio, even if that is the default experience for the domain.
     */
    StudioWebPortal?: StudioWebPortal;
    /**
     * Details about the POSIX identity that is used for file system operations.
     */
    CustomPosixUserConfig?: CustomPosixUserConfig;
    /**
     * The settings for assigning a custom file system to a user profile. Permitted users can access this file system in Amazon SageMaker Studio.
     */
    CustomFileSystemConfigs?: CustomFileSystemConfigs;
    /**
     * Studio settings. If these settings are applied on a user level, they take priority over the settings applied on a domain level.
     */
    StudioWebPortalSettings?: StudioWebPortalSettings;
    /**
     * Indicates whether auto-mounting of an EFS volume is supported for the user profile. The DefaultAsDomain value is only supported for user profiles. Do not use the DefaultAsDomain value when setting this parameter for a domain.
     */
    AutoMountHomeEFS?: AutoMountHomeEFS;
  }
  export type UsersPerStep = number;
  export type UtilizationMetric = number;
  export type UtilizationPercentagePerCore = number;
  export type ValidationFraction = number;
  export type VariantName = string;
  export interface VariantProperty {
    /**
     * The type of variant property. The supported values are:    DesiredInstanceCount: Overrides the existing variant instance counts using the InitialInstanceCount values in the ProductionVariants of CreateEndpointConfig.    DesiredWeight: Overrides the existing variant weights using the InitialVariantWeight values in the ProductionVariants of CreateEndpointConfig.    DataCaptureConfig: (Not currently supported.)  
     */
    VariantPropertyType: VariantPropertyType;
  }
  export type VariantPropertyList = VariantProperty[];
  export type VariantPropertyType = "DesiredInstanceCount"|"DesiredWeight"|"DataCaptureConfig"|string;
  export type VariantStatus = "Creating"|"Updating"|"Deleting"|"ActivatingTraffic"|"Baking"|string;
  export type VariantStatusMessage = string;
  export type VariantWeight = number;
  export interface VectorConfig {
    /**
     * The number of elements in your vector.
     */
    Dimension: Dimension;
  }
  export type VendorGuidance = "NOT_PROVIDED"|"STABLE"|"TO_BE_ARCHIVED"|"ARCHIVED"|string;
  export type VersionId = string;
  export type VersionedArnOrName = string;
  export interface Vertex {
    /**
     * The Amazon Resource Name (ARN) of the lineage entity resource.
     */
    Arn?: AssociationEntityArn;
    /**
     * The type of the lineage entity resource. For example: DataSet, Model, Endpoint, etc...
     */
    Type?: String40;
    /**
     * The type of resource of the lineage entity.
     */
    LineageType?: LineageType;
  }
  export type Vertices = Vertex[];
  export interface VisibilityConditions {
    /**
     * The key that specifies the tag that you're using to filter the search results. It must be in the following format: Tags.&lt;key&gt;.
     */
    Key?: VisibilityConditionsKey;
    /**
     * The value for the tag that you're using to filter the search results.
     */
    Value?: VisibilityConditionsValue;
  }
  export type VisibilityConditionsKey = string;
  export type VisibilityConditionsList = VisibilityConditions[];
  export type VisibilityConditionsValue = string;
  export type VolumeSizeInGB = number;
  export interface VpcConfig {
    /**
     * The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.
     */
    SecurityGroupIds: VpcSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.
     */
    Subnets: Subnets;
  }
  export type VpcId = string;
  export type VpcOnlyTrustedAccounts = AccountId[];
  export type VpcSecurityGroupIds = SecurityGroupId[];
  export type WaitIntervalInSeconds = number;
  export type WarmPoolResourceStatus = "Available"|"Terminated"|"Reused"|"InUse"|string;
  export interface WarmPoolStatus {
    /**
     * The status of the warm pool.    InUse: The warm pool is in use for the training job.    Available: The warm pool is available to reuse for a matching training job.    Reused: The warm pool moved to a matching training job for reuse.    Terminated: The warm pool is no longer available. Warm pools are unavailable if they are terminated by a user, terminated for a patch update, or terminated for exceeding the specified KeepAlivePeriodInSeconds.  
     */
    Status: WarmPoolResourceStatus;
    /**
     * The billable time in seconds used by the warm pool. Billable time refers to the absolute wall-clock time. Multiply ResourceRetainedBillableTimeInSeconds by the number of instances (InstanceCount) in your training cluster to get the total compute time SageMaker bills you if you run warm pool training. The formula is as follows: ResourceRetainedBillableTimeInSeconds * InstanceCount.
     */
    ResourceRetainedBillableTimeInSeconds?: ResourceRetainedBillableTimeInSeconds;
    /**
     * The name of the matching training job that reused the warm pool.
     */
    ReusedByJob?: TrainingJobName;
  }
  export type WeeklyMaintenanceWindowStart = string;
  export interface WorkerAccessConfiguration {
    /**
     * Defines any Amazon S3 resource constraints.
     */
    S3Presign?: S3Presign;
  }
  export interface Workforce {
    /**
     * The name of the private workforce.
     */
    WorkforceName: WorkforceName;
    /**
     * The Amazon Resource Name (ARN) of the private workforce.
     */
    WorkforceArn: WorkforceArn;
    /**
     * The most recent date that UpdateWorkforce was used to successfully add one or more IP address ranges (CIDRs) to a private workforce's allow list.
     */
    LastUpdatedDate?: Timestamp;
    /**
     * A list of one to ten IP address ranges (CIDRs) to be added to the workforce allow list. By default, a workforce isn't restricted to specific IP addresses.
     */
    SourceIpConfig?: SourceIpConfig;
    /**
     * The subdomain for your OIDC Identity Provider.
     */
    SubDomain?: String;
    /**
     * The configuration of an Amazon Cognito workforce. A single Cognito workforce is created using and corresponds to a single  Amazon Cognito user pool.
     */
    CognitoConfig?: CognitoConfig;
    /**
     * The configuration of an OIDC Identity Provider (IdP) private workforce.
     */
    OidcConfig?: OidcConfigForResponse;
    /**
     * The date that the workforce is created.
     */
    CreateDate?: Timestamp;
    /**
     * The configuration of a VPC workforce.
     */
    WorkforceVpcConfig?: WorkforceVpcConfigResponse;
    /**
     * The status of your workforce.
     */
    Status?: WorkforceStatus;
    /**
     * The reason your workforce failed.
     */
    FailureReason?: WorkforceFailureReason;
  }
  export type WorkforceArn = string;
  export type WorkforceFailureReason = string;
  export type WorkforceName = string;
  export type WorkforceSecurityGroupId = string;
  export type WorkforceSecurityGroupIds = WorkforceSecurityGroupId[];
  export type WorkforceStatus = "Initializing"|"Updating"|"Deleting"|"Failed"|"Active"|string;
  export type WorkforceSubnetId = string;
  export type WorkforceSubnets = WorkforceSubnetId[];
  export interface WorkforceVpcConfigRequest {
    /**
     * The ID of the VPC that the workforce uses for communication.
     */
    VpcId?: WorkforceVpcId;
    /**
     * The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
     */
    SecurityGroupIds?: WorkforceSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC that you want to connect.
     */
    Subnets?: WorkforceSubnets;
  }
  export interface WorkforceVpcConfigResponse {
    /**
     * The ID of the VPC that the workforce uses for communication.
     */
    VpcId: WorkforceVpcId;
    /**
     * The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.
     */
    SecurityGroupIds: WorkforceSecurityGroupIds;
    /**
     * The ID of the subnets in the VPC that you want to connect.
     */
    Subnets: WorkforceSubnets;
    /**
     * The IDs for the VPC service endpoints of your VPC workforce when it is created and updated.
     */
    VpcEndpointId?: WorkforceVpcEndpointId;
  }
  export type WorkforceVpcEndpointId = string;
  export type WorkforceVpcId = string;
  export type Workforces = Workforce[];
  export interface WorkspaceSettings {
    /**
     * The Amazon S3 bucket used to store artifacts generated by Canvas. Updating the Amazon S3 location impacts existing configuration settings, and Canvas users no longer have access to their artifacts. Canvas users must log out and log back in to apply the new location.
     */
    S3ArtifactPath?: S3Uri;
    /**
     * The Amazon Web Services Key Management Service (KMS) encryption key ID that is used to encrypt artifacts generated by Canvas in the Amazon S3 bucket.
     */
    S3KmsKeyId?: KmsKeyId;
  }
  export interface Workteam {
    /**
     * The name of the work team.
     */
    WorkteamName: WorkteamName;
    /**
     * A list of MemberDefinition objects that contains objects that identify the workers that make up the work team.  Workforces can be created using Amazon Cognito or your own OIDC Identity Provider (IdP). For private workforces created using Amazon Cognito use CognitoMemberDefinition. For workforces created using your own OIDC identity provider (IdP) use OidcMemberDefinition.
     */
    MemberDefinitions: MemberDefinitions;
    /**
     * The Amazon Resource Name (ARN) that identifies the work team.
     */
    WorkteamArn: WorkteamArn;
    /**
     * The Amazon Resource Name (ARN) of the workforce.
     */
    WorkforceArn?: WorkforceArn;
    /**
     * The Amazon Marketplace identifier for a vendor's work team.
     */
    ProductListingIds?: ProductListings;
    /**
     * A description of the work team.
     */
    Description: String200;
    /**
     * The URI of the labeling job's user interface. Workers open this URI to start labeling your data objects.
     */
    SubDomain?: String;
    /**
     * The date and time that the work team was created (timestamp).
     */
    CreateDate?: Timestamp;
    /**
     * The date and time that the work team was last updated (timestamp).
     */
    LastUpdatedDate?: Timestamp;
    /**
     * Configures SNS notifications of available or expiring work items for work teams.
     */
    NotificationConfiguration?: NotificationConfiguration;
    /**
     * Describes any access constraints that have been defined for Amazon S3 resources.
     */
    WorkerAccessConfiguration?: WorkerAccessConfiguration;
  }
  export type WorkteamArn = string;
  export type WorkteamName = string;
  export type Workteams = Workteam[];
  /**
   * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
   */
  export type apiVersion = "2017-07-24"|"latest"|string;
  export interface ClientApiVersions {
    /**
     * A string in YYYY-MM-DD format that represents the latest possible API version that can be used in this service. Specify 'latest' to use the latest possible version.
     */
    apiVersion?: apiVersion;
  }
  export type ClientConfiguration = ServiceConfigurationOptions & ClientApiVersions;
  /**
   * Contains interfaces for use with the SageMaker client.
   */
  export import Types = SageMaker;
}
export = SageMaker;
