Class: Aws::SageMaker::Client

Inherits:

Seahorse::Client::Base

• Object
• Seahorse::Client::Base
• Aws::SageMaker::Client

Includes:

ClientStubs

Defined in:

gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb

Overview

An API client for SageMaker. To construct a client, you need to configure a :region and :credentials.

client = Aws::SageMaker::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

For details on configuring region and credentials see the developer guide.

See #initialize for a full list of supported configuration options.

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations

• #add_association(params = {}) ⇒ Types::AddAssociationResponse

  Creates an association between the source and the destination.

• #add_tags(params = {}) ⇒ Types::AddTagsOutput

  Adds or overwrites one or more tags for the specified SageMaker resource.

• #associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse

  Associates a trial component with a trial.

• #attach_cluster_node_network_interface(params = {}) ⇒ Types::AttachClusterNodeNetworkInterfaceResponse

  Attaches an elastic network interface (ENI) to a node in a HyperPod cluster.

• #attach_cluster_node_volume(params = {}) ⇒ Types::AttachClusterNodeVolumeResponse

  Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster.

• #batch_add_cluster_nodes(params = {}) ⇒ Types::BatchAddClusterNodesResponse

  Adds nodes to a HyperPod cluster by incrementing the target count for one or more instance groups.

• #batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse

  Deletes specific nodes within a SageMaker HyperPod cluster.

• #batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput

  This action batch describes a list of versioned model packages.

• #batch_reboot_cluster_nodes(params = {}) ⇒ Types::BatchRebootClusterNodesResponse

  Reboots specific nodes within a SageMaker HyperPod cluster using a soft recovery mechanism.

• #batch_replace_cluster_nodes(params = {}) ⇒ Types::BatchReplaceClusterNodesResponse

  Replaces specific nodes within a SageMaker HyperPod cluster with new hardware.

• #create_action(params = {}) ⇒ Types::CreateActionResponse

  Creates an action.

• #create_ai_benchmark_job(params = {}) ⇒ Types::CreateAIBenchmarkJobResponse

  Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration.

• #create_ai_recommendation_job(params = {}) ⇒ Types::CreateAIRecommendationJobResponse

  Creates a recommendation job that generates intelligent optimization recommendations for generative AI inference deployments.

• #create_ai_workload_config(params = {}) ⇒ Types::CreateAIWorkloadConfigResponse

  Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.

• #create_algorithm(params = {}) ⇒ Types::CreateAlgorithmOutput

  Create a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.

• #create_app(params = {}) ⇒ Types::CreateAppResponse

  Creates a running app for the specified UserProfile.

• #create_app_image_config(params = {}) ⇒ Types::CreateAppImageConfigResponse

  Creates a configuration for running a SageMaker AI image as a KernelGateway app.

• #create_artifact(params = {}) ⇒ Types::CreateArtifactResponse

  Creates an artifact.

• #create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse

  Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

• #create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response

  Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

• #create_cluster(params = {}) ⇒ Types::CreateClusterResponse

  Creates an Amazon SageMaker HyperPod cluster.

• #create_cluster_scheduler_config(params = {}) ⇒ Types::CreateClusterSchedulerConfigResponse

  Create cluster policy configuration.

• #create_code_repository(params = {}) ⇒ Types::CreateCodeRepositoryOutput

  Creates a Git repository as a resource in your SageMaker AI account.

• #create_compilation_job(params = {}) ⇒ Types::CreateCompilationJobResponse

  Starts a model compilation job.

• #create_compute_quota(params = {}) ⇒ Types::CreateComputeQuotaResponse

  Create compute allocation definition.

• #create_context(params = {}) ⇒ Types::CreateContextResponse

  Creates a context.

• #create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse

  Creates a definition for a job that monitors data quality and drift.

• #create_device_fleet(params = {}) ⇒ Struct

  Creates a device fleet.

• #create_domain(params = {}) ⇒ Types::CreateDomainResponse

  Creates a Domain.

• #create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse

  Creates an edge deployment plan, consisting of multiple stages.

• #create_edge_deployment_stage(params = {}) ⇒ Struct

  Creates a new stage in an existing edge deployment plan.

• #create_edge_packaging_job(params = {}) ⇒ Struct

  Starts a SageMaker Edge Manager model packaging job.

• #create_endpoint(params = {}) ⇒ Types::CreateEndpointOutput

  Creates an endpoint using the endpoint configuration specified in the request.

• #create_endpoint_config(params = {}) ⇒ Types::CreateEndpointConfigOutput

  Creates an endpoint configuration that SageMaker hosting services uses to deploy models.

• #create_experiment(params = {}) ⇒ Types::CreateExperimentResponse

  Creates a SageMaker experiment.

• #create_feature_group(params = {}) ⇒ Types::CreateFeatureGroupResponse

  Create a new FeatureGroup.

• #create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse

  Creates a flow definition.

• #create_hub(params = {}) ⇒ Types::CreateHubResponse

  Create a hub.

• #create_hub_content_presigned_urls(params = {}) ⇒ Types::CreateHubContentPresignedUrlsResponse

  Creates presigned URLs for accessing hub content artifacts.

• #create_hub_content_reference(params = {}) ⇒ Types::CreateHubContentReferenceResponse

  Create a hub content reference in order to add a model in the JumpStart public hub to a private hub.

• #create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse

  Defines the settings you will use for the human review workflow user interface.

• #create_hyper_parameter_tuning_job(params = {}) ⇒ Types::CreateHyperParameterTuningJobResponse

  Starts a hyperparameter tuning job.

• #create_image(params = {}) ⇒ Types::CreateImageResponse

  Creates a custom SageMaker AI image.

• #create_image_version(params = {}) ⇒ Types::CreateImageVersionResponse

  Creates a version of the SageMaker AI image specified by ImageName.

• #create_inference_component(params = {}) ⇒ Types::CreateInferenceComponentOutput

  Creates an inference component, which is a SageMaker AI hosting object that you can use to deploy a model to an endpoint.

• #create_inference_experiment(params = {}) ⇒ Types::CreateInferenceExperimentResponse

  Creates an inference experiment using the configurations specified in the request.

• #create_inference_recommendations_job(params = {}) ⇒ Types::CreateInferenceRecommendationsJobResponse

  Starts a recommendation job.

• #create_job(params = {}) ⇒ Types::CreateJobResponse

  Creates a model customization job in Amazon SageMaker.

• #create_labeling_job(params = {}) ⇒ Types::CreateLabelingJobResponse

  Creates a job that uses workers to label the data objects in your input dataset.

• #create_mlflow_app(params = {}) ⇒ Types::CreateMlflowAppResponse

  Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.

• #create_mlflow_tracking_server(params = {}) ⇒ Types::CreateMlflowTrackingServerResponse

  Creates an MLflow Tracking Server using a general purpose Amazon S3 bucket as the artifact store.

• #create_model(params = {}) ⇒ Types::CreateModelOutput

  Creates a model in SageMaker.

• #create_model_bias_job_definition(params = {}) ⇒ Types::CreateModelBiasJobDefinitionResponse

  Creates the definition for a model bias job.

• #create_model_card(params = {}) ⇒ Types::CreateModelCardResponse

  Creates an Amazon SageMaker Model Card.

• #create_model_card_export_job(params = {}) ⇒ Types::CreateModelCardExportJobResponse

  Creates an Amazon SageMaker Model Card export job.

• #create_model_explainability_job_definition(params = {}) ⇒ Types::CreateModelExplainabilityJobDefinitionResponse

  Creates the definition for a model explainability job.

• #create_model_package(params = {}) ⇒ Types::CreateModelPackageOutput

  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.

• #create_model_package_group(params = {}) ⇒ Types::CreateModelPackageGroupOutput

  Creates a model group.

• #create_model_quality_job_definition(params = {}) ⇒ Types::CreateModelQualityJobDefinitionResponse

  Creates a definition for a job that monitors model quality and drift.

• #create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse

  Creates a schedule that regularly starts Amazon SageMaker AI Processing Jobs to monitor the data captured for an Amazon SageMaker AI Endpoint.

• #create_notebook_instance(params = {}) ⇒ Types::CreateNotebookInstanceOutput

  Creates an SageMaker AI notebook instance.

• #create_notebook_instance_lifecycle_config(params = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput

  Creates a lifecycle configuration that you can associate with a notebook instance.

• #create_optimization_job(params = {}) ⇒ Types::CreateOptimizationJobResponse

  Creates a job that optimizes a model for inference performance.

• #create_partner_app(params = {}) ⇒ Types::CreatePartnerAppResponse

  Creates an Amazon SageMaker Partner AI App.

• #create_partner_app_presigned_url(params = {}) ⇒ Types::CreatePartnerAppPresignedUrlResponse

  Creates a presigned URL to access an Amazon SageMaker Partner AI App.

• #create_pipeline(params = {}) ⇒ Types::CreatePipelineResponse

  Creates a pipeline using a JSON pipeline definition.

• #create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse

  Creates a URL for a specified UserProfile in a Domain.

• #create_presigned_mlflow_app_url(params = {}) ⇒ Types::CreatePresignedMlflowAppUrlResponse

  Returns a presigned URL that you can use to connect to the MLflow UI attached to your MLflow App.

• #create_presigned_mlflow_tracking_server_url(params = {}) ⇒ Types::CreatePresignedMlflowTrackingServerUrlResponse

  Returns a presigned URL that you can use to connect to the MLflow UI attached to your tracking server.

• #create_presigned_notebook_instance_url(params = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput

  Returns a URL that you can use to connect to the Jupyter server from a notebook instance.

• #create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse

  Creates a processing job.

• #create_project(params = {}) ⇒ Types::CreateProjectOutput

  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.

• #create_space(params = {}) ⇒ Types::CreateSpaceResponse

  Creates a private space or a space used for real time collaboration in a domain.

• #create_studio_lifecycle_config(params = {}) ⇒ Types::CreateStudioLifecycleConfigResponse

  Creates a new Amazon SageMaker AI Studio Lifecycle Configuration.

• #create_training_job(params = {}) ⇒ Types::CreateTrainingJobResponse

  Starts a model training job.

• #create_training_plan(params = {}) ⇒ Types::CreateTrainingPlanResponse

  Creates a new training plan in SageMaker to reserve compute capacity.

• #create_transform_job(params = {}) ⇒ Types::CreateTransformJobResponse

  Starts a transform job.

• #create_trial(params = {}) ⇒ Types::CreateTrialResponse

  Creates an SageMaker trial.

• #create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse

  Creates a trial component, which is a stage of a machine learning trial.

• #create_user_profile(params = {}) ⇒ Types::CreateUserProfileResponse

  Creates a user profile.

• #create_workforce(params = {}) ⇒ Types::CreateWorkforceResponse

  Use this operation to create a workforce.

• #create_workteam(params = {}) ⇒ Types::CreateWorkteamResponse

  Creates a new work team for labeling your data.

• #delete_action(params = {}) ⇒ Types::DeleteActionResponse

  Deletes an action.

• #delete_ai_benchmark_job(params = {}) ⇒ Types::DeleteAIBenchmarkJobResponse

  Deletes the specified AI benchmark job.

• #delete_ai_recommendation_job(params = {}) ⇒ Types::DeleteAIRecommendationJobResponse

  Deletes the specified AI recommendation job.

• #delete_ai_workload_config(params = {}) ⇒ Types::DeleteAIWorkloadConfigResponse

  Deletes the specified AI workload configuration.

• #delete_algorithm(params = {}) ⇒ Struct

  Removes the specified algorithm from your account.

• #delete_app(params = {}) ⇒ Struct

  Used to stop and delete an app.

• #delete_app_image_config(params = {}) ⇒ Struct

  Deletes an AppImageConfig.

• #delete_artifact(params = {}) ⇒ Types::DeleteArtifactResponse

  Deletes an artifact.

• #delete_association(params = {}) ⇒ Types::DeleteAssociationResponse

  Deletes an association.

• #delete_cluster(params = {}) ⇒ Types::DeleteClusterResponse

  Delete a SageMaker HyperPod cluster.

• #delete_cluster_scheduler_config(params = {}) ⇒ Struct

  Deletes the cluster policy of the cluster.

• #delete_code_repository(params = {}) ⇒ Struct

  Deletes the specified Git repository from your account.

• #delete_compilation_job(params = {}) ⇒ Struct

  Deletes the specified compilation job.

• #delete_compute_quota(params = {}) ⇒ Struct

  Deletes the compute allocation from the cluster.

• #delete_context(params = {}) ⇒ Types::DeleteContextResponse

  Deletes an context.

• #delete_data_quality_job_definition(params = {}) ⇒ Struct

  Deletes a data quality monitoring job definition.

• #delete_device_fleet(params = {}) ⇒ Struct

  Deletes a fleet.

• #delete_domain(params = {}) ⇒ Struct

  Used to delete a domain.

• #delete_edge_deployment_plan(params = {}) ⇒ Struct

  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.

• #delete_edge_deployment_stage(params = {}) ⇒ Struct

  Delete a stage in an edge deployment plan if (and only if) the stage is inactive.

• #delete_endpoint(params = {}) ⇒ Struct

  Deletes an endpoint.

• #delete_endpoint_config(params = {}) ⇒ Struct

  Deletes an endpoint configuration.

• #delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse

  Deletes an SageMaker experiment.

• #delete_feature_group(params = {}) ⇒ Struct

  Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup.

• #delete_flow_definition(params = {}) ⇒ Struct

  Deletes the specified flow definition.

• #delete_hub(params = {}) ⇒ Struct

  Delete a hub.

• #delete_hub_content(params = {}) ⇒ Struct

  Delete the contents of a hub.

• #delete_hub_content_reference(params = {}) ⇒ Struct

  Delete a hub content reference in order to remove a model from a private hub.

• #delete_human_task_ui(params = {}) ⇒ Struct

  Use this operation to delete a human task user interface (worker task template).

• #delete_hyper_parameter_tuning_job(params = {}) ⇒ Struct

  Deletes a hyperparameter tuning job.

• #delete_image(params = {}) ⇒ Struct

  Deletes a SageMaker AI image and all versions of the image.

• #delete_image_version(params = {}) ⇒ Struct

  Deletes a version of a SageMaker AI image.

• #delete_inference_component(params = {}) ⇒ Struct

  Deletes an inference component.

• #delete_inference_experiment(params = {}) ⇒ Types::DeleteInferenceExperimentResponse

  Deletes an inference experiment.

• #delete_job(params = {}) ⇒ Struct

  Deletes a job.

• #delete_mlflow_app(params = {}) ⇒ Types::DeleteMlflowAppResponse

  Deletes an MLflow App.

• #delete_mlflow_tracking_server(params = {}) ⇒ Types::DeleteMlflowTrackingServerResponse

  Deletes an MLflow Tracking Server.

• #delete_model(params = {}) ⇒ Struct

  Deletes a model.

• #delete_model_bias_job_definition(params = {}) ⇒ Struct

  Deletes an Amazon SageMaker AI model bias job definition.

• #delete_model_card(params = {}) ⇒ Struct

  Deletes an Amazon SageMaker Model Card.

• #delete_model_explainability_job_definition(params = {}) ⇒ Struct

  Deletes an Amazon SageMaker AI model explainability job definition.

• #delete_model_package(params = {}) ⇒ Struct

  Deletes a model package.

• #delete_model_package_group(params = {}) ⇒ Struct

  Deletes the specified model group.

• #delete_model_package_group_policy(params = {}) ⇒ Struct

  Deletes a model group resource policy.

• #delete_model_quality_job_definition(params = {}) ⇒ Struct

  Deletes the secified model quality monitoring job definition.

• #delete_monitoring_schedule(params = {}) ⇒ Struct

  Deletes a monitoring schedule.

• #delete_notebook_instance(params = {}) ⇒ Struct

  Deletes an SageMaker AI notebook instance.

• #delete_notebook_instance_lifecycle_config(params = {}) ⇒ Struct

  Deletes a notebook instance lifecycle configuration.

• #delete_optimization_job(params = {}) ⇒ Struct

  Deletes an optimization job.

• #delete_partner_app(params = {}) ⇒ Types::DeletePartnerAppResponse

  Deletes a SageMaker Partner AI App.

• #delete_pipeline(params = {}) ⇒ Types::DeletePipelineResponse

  Deletes a pipeline if there are no running instances of the pipeline.

• #delete_processing_job(params = {}) ⇒ Struct

  Deletes a processing job.

• #delete_project(params = {}) ⇒ Struct

  Delete the specified project.

• #delete_space(params = {}) ⇒ Struct

  Used to delete a space.

• #delete_studio_lifecycle_config(params = {}) ⇒ Struct

  Deletes the Amazon SageMaker AI Studio Lifecycle Configuration.

• #delete_tags(params = {}) ⇒ Struct

  Deletes the specified tags from an SageMaker resource.

• #delete_training_job(params = {}) ⇒ Struct

  Deletes a training job.

• #delete_trial(params = {}) ⇒ Types::DeleteTrialResponse

  Deletes the specified trial.

• #delete_trial_component(params = {}) ⇒ Types::DeleteTrialComponentResponse

  Deletes the specified trial component.

• #delete_user_profile(params = {}) ⇒ Struct

  Deletes a user profile.

• #delete_workforce(params = {}) ⇒ Struct

  Use this operation to delete a workforce.

• #delete_workteam(params = {}) ⇒ Types::DeleteWorkteamResponse

  Deletes an existing work team.

• #deregister_devices(params = {}) ⇒ Struct

  Deregisters the specified devices.

• #describe_action(params = {}) ⇒ Types::DescribeActionResponse

  Describes an action.

• #describe_ai_benchmark_job(params = {}) ⇒ Types::DescribeAIBenchmarkJobResponse

  Returns details of an AI benchmark job, including its status, configuration, target endpoint, and timing information.

• #describe_ai_recommendation_job(params = {}) ⇒ Types::DescribeAIRecommendationJobResponse

  Returns details of an AI recommendation job, including its status, model source, performance targets, optimization recommendations, and deployment configurations.

• #describe_ai_workload_config(params = {}) ⇒ Types::DescribeAIWorkloadConfigResponse

  Returns details of an AI workload configuration, including the dataset configuration, benchmark tool settings, tags, and creation time.

• #describe_algorithm(params = {}) ⇒ Types::DescribeAlgorithmOutput

  Returns a description of the specified algorithm that is in your account.

• #describe_app(params = {}) ⇒ Types::DescribeAppResponse

  Describes the app.

• #describe_app_image_config(params = {}) ⇒ Types::DescribeAppImageConfigResponse

  Describes an AppImageConfig.

• #describe_artifact(params = {}) ⇒ Types::DescribeArtifactResponse

  Describes an artifact.

• #describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse

  Returns information about an AutoML job created by calling [CreateAutoMLJob][1].

• #describe_auto_ml_job_v2(params = {}) ⇒ Types::DescribeAutoMLJobV2Response

  Returns information about an AutoML job created by calling [CreateAutoMLJobV2][1] or [CreateAutoMLJob][2].

• #describe_cluster(params = {}) ⇒ Types::DescribeClusterResponse

  Retrieves information of a SageMaker HyperPod cluster.

• #describe_cluster_event(params = {}) ⇒ Types::DescribeClusterEventResponse

  Retrieves detailed information about a specific event for a given HyperPod cluster.

• #describe_cluster_node(params = {}) ⇒ Types::DescribeClusterNodeResponse

  Retrieves information of a node (also called a instance interchangeably) of a SageMaker HyperPod cluster.

• #describe_cluster_scheduler_config(params = {}) ⇒ Types::DescribeClusterSchedulerConfigResponse

  Description of the cluster policy.

• #describe_code_repository(params = {}) ⇒ Types::DescribeCodeRepositoryOutput

  Gets details about the specified Git repository.

• #describe_compilation_job(params = {}) ⇒ Types::DescribeCompilationJobResponse

  Returns information about a model compilation job.

• #describe_compute_quota(params = {}) ⇒ Types::DescribeComputeQuotaResponse

  Description of the compute allocation definition.

• #describe_context(params = {}) ⇒ Types::DescribeContextResponse

  Describes a context.

• #describe_data_quality_job_definition(params = {}) ⇒ Types::DescribeDataQualityJobDefinitionResponse

  Gets the details of a data quality monitoring job definition.

• #describe_device(params = {}) ⇒ Types::DescribeDeviceResponse

  Describes the device.

• #describe_device_fleet(params = {}) ⇒ Types::DescribeDeviceFleetResponse

  A description of the fleet the device belongs to.

• #describe_domain(params = {}) ⇒ Types::DescribeDomainResponse

  The description of the domain.

• #describe_edge_deployment_plan(params = {}) ⇒ Types::DescribeEdgeDeploymentPlanResponse

  Describes an edge deployment plan with deployment status per stage.

• #describe_edge_packaging_job(params = {}) ⇒ Types::DescribeEdgePackagingJobResponse

  A description of edge packaging jobs.

• #describe_endpoint(params = {}) ⇒ Types::DescribeEndpointOutput

  Returns the description of an endpoint.

• #describe_endpoint_config(params = {}) ⇒ Types::DescribeEndpointConfigOutput

  Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

• #describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse

  Provides a list of an experiment's properties.

• #describe_feature_group(params = {}) ⇒ Types::DescribeFeatureGroupResponse

  Use this operation to describe a FeatureGroup.

• #describe_feature_metadata(params = {}) ⇒ Types::DescribeFeatureMetadataResponse

  Shows the metadata for a feature within a feature group.

• #describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse

  Returns information about the specified flow definition.

• #describe_hub(params = {}) ⇒ Types::DescribeHubResponse

  Describes a hub.

• #describe_hub_content(params = {}) ⇒ Types::DescribeHubContentResponse

  Describe the content of a hub.

• #describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse

  Returns information about the requested human task user interface (worker task template).

• #describe_hyper_parameter_tuning_job(params = {}) ⇒ Types::DescribeHyperParameterTuningJobResponse

  Returns a description of a hyperparameter tuning job, depending on the fields selected.

• #describe_image(params = {}) ⇒ Types::DescribeImageResponse

  Describes a SageMaker AI image.

• #describe_image_version(params = {}) ⇒ Types::DescribeImageVersionResponse

  Describes a version of a SageMaker AI image.

• #describe_inference_component(params = {}) ⇒ Types::DescribeInferenceComponentOutput

  Returns information about an inference component.

• #describe_inference_experiment(params = {}) ⇒ Types::DescribeInferenceExperimentResponse

  Returns details about an inference experiment.

• #describe_inference_recommendations_job(params = {}) ⇒ Types::DescribeInferenceRecommendationsJobResponse

  Provides the results of the Inference Recommender job.

• #describe_job(params = {}) ⇒ Types::DescribeJobResponse

  Returns detailed information about a job, including its current status, secondary status, configuration, and timestamps.

• #describe_job_schema_version(params = {}) ⇒ Types::DescribeJobSchemaVersionResponse

  Returns the JSON schema for a specified job category and schema version.

• #describe_labeling_job(params = {}) ⇒ Types::DescribeLabelingJobResponse

  Gets information about a labeling job.

• #describe_lineage_group(params = {}) ⇒ Types::DescribeLineageGroupResponse

  Provides a list of properties for the requested lineage group.

• #describe_mlflow_app(params = {}) ⇒ Types::DescribeMlflowAppResponse

  Returns information about an MLflow App.

• #describe_mlflow_tracking_server(params = {}) ⇒ Types::DescribeMlflowTrackingServerResponse

  Returns information about an MLflow Tracking Server.

• #describe_model(params = {}) ⇒ Types::DescribeModelOutput

  Describes a model that you created using the CreateModel API.

• #describe_model_bias_job_definition(params = {}) ⇒ Types::DescribeModelBiasJobDefinitionResponse

  Returns a description of a model bias job definition.

• #describe_model_card(params = {}) ⇒ Types::DescribeModelCardResponse

  Describes the content, creation time, and security configuration of an Amazon SageMaker Model Card.

• #describe_model_card_export_job(params = {}) ⇒ Types::DescribeModelCardExportJobResponse

  Describes an Amazon SageMaker Model Card export job.

• #describe_model_explainability_job_definition(params = {}) ⇒ Types::DescribeModelExplainabilityJobDefinitionResponse

  Returns a description of a model explainability job definition.

• #describe_model_package(params = {}) ⇒ Types::DescribeModelPackageOutput

  Returns a description of the specified model package, which is used to create SageMaker models or list them on Amazon Web Services Marketplace.

• #describe_model_package_group(params = {}) ⇒ Types::DescribeModelPackageGroupOutput

  Gets a description for the specified model group.

• #describe_model_quality_job_definition(params = {}) ⇒ Types::DescribeModelQualityJobDefinitionResponse

  Returns a description of a model quality job definition.

• #describe_monitoring_schedule(params = {}) ⇒ Types::DescribeMonitoringScheduleResponse

  Describes the schedule for a monitoring job.

• #describe_notebook_instance(params = {}) ⇒ Types::DescribeNotebookInstanceOutput

  Returns information about a notebook instance.

• #describe_notebook_instance_lifecycle_config(params = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput

  Returns a description of a notebook instance lifecycle configuration.

• #describe_optimization_job(params = {}) ⇒ Types::DescribeOptimizationJobResponse

  Provides the properties of the specified optimization job.

• #describe_partner_app(params = {}) ⇒ Types::DescribePartnerAppResponse

  Gets information about a SageMaker Partner AI App.

• #describe_pipeline(params = {}) ⇒ Types::DescribePipelineResponse

  Describes the details of a pipeline.

• #describe_pipeline_definition_for_execution(params = {}) ⇒ Types::DescribePipelineDefinitionForExecutionResponse

  Describes the details of an execution's pipeline definition.

• #describe_pipeline_execution(params = {}) ⇒ Types::DescribePipelineExecutionResponse

  Describes the details of a pipeline execution.

• #describe_processing_job(params = {}) ⇒ Types::DescribeProcessingJobResponse

  Returns a description of a processing job.

• #describe_project(params = {}) ⇒ Types::DescribeProjectOutput

  Describes the details of a project.

• #describe_reserved_capacity(params = {}) ⇒ Types::DescribeReservedCapacityResponse

  Retrieves details about a reserved capacity.

• #describe_space(params = {}) ⇒ Types::DescribeSpaceResponse

  Describes the space.

• #describe_studio_lifecycle_config(params = {}) ⇒ Types::DescribeStudioLifecycleConfigResponse

  Describes the Amazon SageMaker AI Studio Lifecycle Configuration.

• #describe_subscribed_workteam(params = {}) ⇒ Types::DescribeSubscribedWorkteamResponse

  Gets information about a work team provided by a vendor.

• #describe_training_job(params = {}) ⇒ Types::DescribeTrainingJobResponse

  Returns information about a training job.

• #describe_training_plan(params = {}) ⇒ Types::DescribeTrainingPlanResponse

  Retrieves detailed information about a specific training plan.

• #describe_training_plan_extension_history(params = {}) ⇒ Types::DescribeTrainingPlanExtensionHistoryResponse

  Retrieves the extension history for a specified training plan.

• #describe_transform_job(params = {}) ⇒ Types::DescribeTransformJobResponse

  Returns information about a transform job.

• #describe_trial(params = {}) ⇒ Types::DescribeTrialResponse

  Provides a list of a trial's properties.

• #describe_trial_component(params = {}) ⇒ Types::DescribeTrialComponentResponse

  Provides a list of a trials component's properties.

• #describe_user_profile(params = {}) ⇒ Types::DescribeUserProfileResponse

  Describes a user profile.

• #describe_workforce(params = {}) ⇒ Types::DescribeWorkforceResponse

  Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges ([CIDRs][1]).

• #describe_workteam(params = {}) ⇒ Types::DescribeWorkteamResponse

  Gets information about a specific work team.

• #detach_cluster_node_volume(params = {}) ⇒ Types::DetachClusterNodeVolumeResponse

  Detaches your Amazon Elastic Block Store (Amazon EBS) volume from a node in your EKS orchestrated SageMaker HyperPod cluster.

• #disable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct

  Disables using Service Catalog in SageMaker.

• #disassociate_trial_component(params = {}) ⇒ Types::DisassociateTrialComponentResponse

  Disassociates a trial component from a trial.

• #enable_sagemaker_servicecatalog_portfolio(params = {}) ⇒ Struct

  Enables using Service Catalog in SageMaker.

• #extend_training_plan(params = {}) ⇒ Types::ExtendTrainingPlanResponse

  Extends an existing training plan by purchasing an extension offering.

• #get_device_fleet_report(params = {}) ⇒ Types::GetDeviceFleetReportResponse

  Describes a fleet.

• #get_lineage_group_policy(params = {}) ⇒ Types::GetLineageGroupPolicyResponse

  The resource policy for the lineage group.

• #get_model_package_group_policy(params = {}) ⇒ Types::GetModelPackageGroupPolicyOutput

  Gets a resource policy that manages access for a model group.

• #get_sagemaker_servicecatalog_portfolio_status(params = {}) ⇒ Types::GetSagemakerServicecatalogPortfolioStatusOutput

  Gets the status of Service Catalog in SageMaker.

• #get_scaling_configuration_recommendation(params = {}) ⇒ Types::GetScalingConfigurationRecommendationResponse

  Starts an Amazon SageMaker Inference Recommender autoscaling recommendation job.

• #get_search_suggestions(params = {}) ⇒ Types::GetSearchSuggestionsResponse

  An auto-complete API for the search functionality in the SageMaker console.

• #import_hub_content(params = {}) ⇒ Types::ImportHubContentResponse

  Import hub content.

• #list_actions(params = {}) ⇒ Types::ListActionsResponse

  Lists the actions in your account and their properties.

• #list_ai_benchmark_jobs(params = {}) ⇒ Types::ListAIBenchmarkJobsResponse

  Returns a list of AI benchmark jobs in your account.