AmazonSageMaker (AWS SDK for Java

All Known Subinterfaces:

AmazonSageMakerAsync

All Known Implementing Classes:

AbstractAmazonSageMaker, AbstractAmazonSageMakerAsync, AmazonSageMakerAsyncClient, AmazonSageMakerClient
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public interface AmazonSageMaker
```
Interface for accessing SageMaker.
Note: Do not directly implement this interface, new methods are added to it regularly. Extend from AbstractAmazonSageMaker instead.

Provides APIs for creating and managing Amazon SageMaker resources.

Other Resources:
- Amazon SageMaker Developer Guide
- Amazon Augmented AI Runtime API Reference

Field Summary

Fields
Modifier and Type Field and Description

static String ENDPOINT_PREFIX
The region metadata service name for computing region endpoints.

Fields
Modifier and Type	Field and Description
`static String`	`ENDPOINT_PREFIX` The region metadata service name for computing region endpoints.

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`AddTagsResult`	`addTags(AddTagsRequest addTagsRequest)` Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
`AssociateTrialComponentResult`	`associateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest)` Associates a trial component with a trial.
`CreateAlgorithmResult`	`createAlgorithm(CreateAlgorithmRequest createAlgorithmRequest)` Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
`CreateAppResult`	`createApp(CreateAppRequest createAppRequest)` Creates a running App for the specified UserProfile.
`CreateAppImageConfigResult`	`createAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest)` Creates a configuration for running an Amazon SageMaker image as a KernelGateway app.
`CreateAutoMLJobResult`	`createAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest)` Creates an Autopilot job.
`CreateCodeRepositoryResult`	`createCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest)` Creates a Git repository as a resource in your Amazon SageMaker account.
`CreateCompilationJobResult`	`createCompilationJob(CreateCompilationJobRequest createCompilationJobRequest)` Starts a model compilation job.
`CreateDomainResult`	`createDomain(CreateDomainRequest createDomainRequest)` Creates a `Domain` used by Amazon SageMaker Studio.
`CreateEndpointResult`	`createEndpoint(CreateEndpointRequest createEndpointRequest)` Creates an endpoint using the endpoint configuration specified in the request.
`CreateEndpointConfigResult`	`createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)` Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
`CreateExperimentResult`	`createExperiment(CreateExperimentRequest createExperimentRequest)` Creates an SageMaker experiment.
`CreateFlowDefinitionResult`	`createFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest)` Creates a flow definition.
`CreateHumanTaskUiResult`	`createHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest)` Defines the settings you will use for the human review workflow user interface.
`CreateHyperParameterTuningJobResult`	`createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)` Starts a hyperparameter tuning job.
`CreateImageResult`	`createImage(CreateImageRequest createImageRequest)` Creates a SageMaker `Image`.
`CreateImageVersionResult`	`createImageVersion(CreateImageVersionRequest createImageVersionRequest)` Creates a version of the SageMaker image specified by `ImageName`.
`CreateLabelingJobResult`	`createLabelingJob(CreateLabelingJobRequest createLabelingJobRequest)` Creates a job that uses workers to label the data objects in your input dataset.
`CreateModelResult`	`createModel(CreateModelRequest createModelRequest)` Creates a model in Amazon SageMaker.
`CreateModelPackageResult`	`createModelPackage(CreateModelPackageRequest createModelPackageRequest)` Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace.
`CreateMonitoringScheduleResult`	`createMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)` Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
`CreateNotebookInstanceResult`	`createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)` Creates an Amazon SageMaker notebook instance.
`CreateNotebookInstanceLifecycleConfigResult`	`createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)` Creates a lifecycle configuration that you can associate with a notebook instance.
`CreatePresignedDomainUrlResult`	`createPresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)` Creates a URL for a specified UserProfile in a Domain.
`CreatePresignedNotebookInstanceUrlResult`	`createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)` Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
`CreateProcessingJobResult`	`createProcessingJob(CreateProcessingJobRequest createProcessingJobRequest)` Creates a processing job.
`CreateTrainingJobResult`	`createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)` Starts a model training job.
`CreateTransformJobResult`	`createTransformJob(CreateTransformJobRequest createTransformJobRequest)` Starts a transform job.
`CreateTrialResult`	`createTrial(CreateTrialRequest createTrialRequest)` Creates an Amazon SageMaker trial.
`CreateTrialComponentResult`	`createTrialComponent(CreateTrialComponentRequest createTrialComponentRequest)` Creates a trial component, which is a stage of a machine learning trial.
`CreateUserProfileResult`	`createUserProfile(CreateUserProfileRequest createUserProfileRequest)` Creates a user profile.
`CreateWorkforceResult`	`createWorkforce(CreateWorkforceRequest createWorkforceRequest)` Use this operation to create a workforce.
`CreateWorkteamResult`	`createWorkteam(CreateWorkteamRequest createWorkteamRequest)` Creates a new work team for labeling your data.
`DeleteAlgorithmResult`	`deleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest)` Removes the specified algorithm from your account.
`DeleteAppResult`	`deleteApp(DeleteAppRequest deleteAppRequest)` Used to stop and delete an app.
`DeleteAppImageConfigResult`	`deleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest)` Deletes an AppImageConfig.
`DeleteCodeRepositoryResult`	`deleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)` Deletes the specified Git repository from your account.
`DeleteDomainResult`	`deleteDomain(DeleteDomainRequest deleteDomainRequest)` Used to delete a domain.
`DeleteEndpointResult`	`deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)` Deletes an endpoint.
`DeleteEndpointConfigResult`	`deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)` Deletes an endpoint configuration.
`DeleteExperimentResult`	`deleteExperiment(DeleteExperimentRequest deleteExperimentRequest)` Deletes an Amazon SageMaker experiment.
`DeleteFlowDefinitionResult`	`deleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)` Deletes the specified flow definition.
`DeleteHumanTaskUiResult`	`deleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)` Use this operation to delete a human task user interface (worker task template).
`DeleteImageResult`	`deleteImage(DeleteImageRequest deleteImageRequest)` Deletes a SageMaker image and all versions of the image.
`DeleteImageVersionResult`	`deleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest)` Deletes a version of a SageMaker image.
`DeleteModelResult`	`deleteModel(DeleteModelRequest deleteModelRequest)` Deletes a model.
`DeleteModelPackageResult`	`deleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest)` Deletes a model package.
`DeleteMonitoringScheduleResult`	`deleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)` Deletes a monitoring schedule.
`DeleteNotebookInstanceResult`	`deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)` Deletes an Amazon SageMaker notebook instance.
`DeleteNotebookInstanceLifecycleConfigResult`	`deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)` Deletes a notebook instance lifecycle configuration.
`DeleteTagsResult`	`deleteTags(DeleteTagsRequest deleteTagsRequest)` Deletes the specified tags from an Amazon SageMaker resource.
`DeleteTrialResult`	`deleteTrial(DeleteTrialRequest deleteTrialRequest)` Deletes the specified trial.
`DeleteTrialComponentResult`	`deleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest)` Deletes the specified trial component.
`DeleteUserProfileResult`	`deleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest)` Deletes a user profile.
`DeleteWorkforceResult`	`deleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest)` Use this operation to delete a workforce.
`DeleteWorkteamResult`	`deleteWorkteam(DeleteWorkteamRequest deleteWorkteamRequest)` Deletes an existing work team.
`DescribeAlgorithmResult`	`describeAlgorithm(DescribeAlgorithmRequest describeAlgorithmRequest)` Returns a description of the specified algorithm that is in your account.
`DescribeAppResult`	`describeApp(DescribeAppRequest describeAppRequest)` Describes the app.
`DescribeAppImageConfigResult`	`describeAppImageConfig(DescribeAppImageConfigRequest describeAppImageConfigRequest)` Describes an AppImageConfig.
`DescribeAutoMLJobResult`	`describeAutoMLJob(DescribeAutoMLJobRequest describeAutoMLJobRequest)` Returns information about an Amazon SageMaker job.
`DescribeCodeRepositoryResult`	`describeCodeRepository(DescribeCodeRepositoryRequest describeCodeRepositoryRequest)` Gets details about the specified Git repository.
`DescribeCompilationJobResult`	`describeCompilationJob(DescribeCompilationJobRequest describeCompilationJobRequest)` Returns information about a model compilation job.
`DescribeDomainResult`	`describeDomain(DescribeDomainRequest describeDomainRequest)` The description of the domain.
`DescribeEndpointResult`	`describeEndpoint(DescribeEndpointRequest describeEndpointRequest)` Returns the description of an endpoint.
`DescribeEndpointConfigResult`	`describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)` Returns the description of an endpoint configuration created using the `CreateEndpointConfig` API.
`DescribeExperimentResult`	`describeExperiment(DescribeExperimentRequest describeExperimentRequest)` Provides a list of an experiment's properties.
`DescribeFlowDefinitionResult`	`describeFlowDefinition(DescribeFlowDefinitionRequest describeFlowDefinitionRequest)` Returns information about the specified flow definition.
`DescribeHumanTaskUiResult`	`describeHumanTaskUi(DescribeHumanTaskUiRequest describeHumanTaskUiRequest)` Returns information about the requested human task user interface (worker task template).
`DescribeHyperParameterTuningJobResult`	`describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest)` Gets a description of a hyperparameter tuning job.
`DescribeImageResult`	`describeImage(DescribeImageRequest describeImageRequest)` Describes a SageMaker image.
`DescribeImageVersionResult`	`describeImageVersion(DescribeImageVersionRequest describeImageVersionRequest)` Describes a version of a SageMaker image.
`DescribeLabelingJobResult`	`describeLabelingJob(DescribeLabelingJobRequest describeLabelingJobRequest)` Gets information about a labeling job.
`DescribeModelResult`	`describeModel(DescribeModelRequest describeModelRequest)` Describes a model that you created using the `CreateModel` API.
`DescribeModelPackageResult`	`describeModelPackage(DescribeModelPackageRequest describeModelPackageRequest)` Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
`DescribeMonitoringScheduleResult`	`describeMonitoringSchedule(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest)` Describes the schedule for a monitoring job.
`DescribeNotebookInstanceResult`	`describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)` Returns information about a notebook instance.
`DescribeNotebookInstanceLifecycleConfigResult`	`describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest)` Returns a description of a notebook instance lifecycle configuration.
`DescribeProcessingJobResult`	`describeProcessingJob(DescribeProcessingJobRequest describeProcessingJobRequest)` Returns a description of a processing job.
`DescribeSubscribedWorkteamResult`	`describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest)` Gets information about a work team provided by a vendor.
`DescribeTrainingJobResult`	`describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)` Returns information about a training job.
`DescribeTransformJobResult`	`describeTransformJob(DescribeTransformJobRequest describeTransformJobRequest)` Returns information about a transform job.
`DescribeTrialResult`	`describeTrial(DescribeTrialRequest describeTrialRequest)` Provides a list of a trial's properties.
`DescribeTrialComponentResult`	`describeTrialComponent(DescribeTrialComponentRequest describeTrialComponentRequest)` Provides a list of a trials component's properties.
`DescribeUserProfileResult`	`describeUserProfile(DescribeUserProfileRequest describeUserProfileRequest)` Describes a user profile.
`DescribeWorkforceResult`	`describeWorkforce(DescribeWorkforceRequest describeWorkforceRequest)` Lists private workforce information, including workforce name, Amazon Resource Name (ARN), and, if applicable, allowed IP address ranges (CIDRs).
`DescribeWorkteamResult`	`describeWorkteam(DescribeWorkteamRequest describeWorkteamRequest)` Gets information about a specific work team.
`DisassociateTrialComponentResult`	`disassociateTrialComponent(DisassociateTrialComponentRequest disassociateTrialComponentRequest)` Disassociates a trial component from a trial.
`ResponseMetadata`	`getCachedResponseMetadata(AmazonWebServiceRequest request)` Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected.
`GetSearchSuggestionsResult`	`getSearchSuggestions(GetSearchSuggestionsRequest getSearchSuggestionsRequest)` An auto-complete API for the search functionality in the Amazon SageMaker console.
`ListAlgorithmsResult`	`listAlgorithms(ListAlgorithmsRequest listAlgorithmsRequest)` Lists the machine learning algorithms that have been created.
`ListAppImageConfigsResult`	`listAppImageConfigs(ListAppImageConfigsRequest listAppImageConfigsRequest)` Lists the AppImageConfigs in your account and their properties.
`ListAppsResult`	`listApps(ListAppsRequest listAppsRequest)` Lists apps.
`ListAutoMLJobsResult`	`listAutoMLJobs(ListAutoMLJobsRequest listAutoMLJobsRequest)` Request a list of jobs.
`ListCandidatesForAutoMLJobResult`	`listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest)` List the Candidates created for the job.
`ListCodeRepositoriesResult`	`listCodeRepositories(ListCodeRepositoriesRequest listCodeRepositoriesRequest)` Gets a list of the Git repositories in your account.
`ListCompilationJobsResult`	`listCompilationJobs(ListCompilationJobsRequest listCompilationJobsRequest)` Lists model compilation jobs that satisfy various filters.
`ListDomainsResult`	`listDomains(ListDomainsRequest listDomainsRequest)` Lists the domains.
`ListEndpointConfigsResult`	`listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)` Lists endpoint configurations.
`ListEndpointsResult`	`listEndpoints(ListEndpointsRequest listEndpointsRequest)` Lists endpoints.
`ListExperimentsResult`	`listExperiments(ListExperimentsRequest listExperimentsRequest)` Lists all the experiments in your account.
`ListFlowDefinitionsResult`	`listFlowDefinitions(ListFlowDefinitionsRequest listFlowDefinitionsRequest)` Returns information about the flow definitions in your account.
`ListHumanTaskUisResult`	`listHumanTaskUis(ListHumanTaskUisRequest listHumanTaskUisRequest)` Returns information about the human task user interfaces in your account.
`ListHyperParameterTuningJobsResult`	`listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest)` Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
`ListImagesResult`	`listImages(ListImagesRequest listImagesRequest)` Lists the images in your account and their properties.
`ListImageVersionsResult`	`listImageVersions(ListImageVersionsRequest listImageVersionsRequest)` Lists the versions of a specified image and their properties.
`ListLabelingJobsResult`	`listLabelingJobs(ListLabelingJobsRequest listLabelingJobsRequest)` Gets a list of labeling jobs.
`ListLabelingJobsForWorkteamResult`	`listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest)` Gets a list of labeling jobs assigned to a specified work team.
`ListModelPackagesResult`	`listModelPackages(ListModelPackagesRequest listModelPackagesRequest)` Lists the model packages that have been created.
`ListModelsResult`	`listModels(ListModelsRequest listModelsRequest)` Lists models created with the CreateModel API.
`ListMonitoringExecutionsResult`	`listMonitoringExecutions(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest)` Returns list of all monitoring job executions.
`ListMonitoringSchedulesResult`	`listMonitoringSchedules(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest)` Returns list of all monitoring schedules.
`ListNotebookInstanceLifecycleConfigsResult`	`listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest)` Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
`ListNotebookInstancesResult`	`listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)` Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
`ListProcessingJobsResult`	`listProcessingJobs(ListProcessingJobsRequest listProcessingJobsRequest)` Lists processing jobs that satisfy various filters.
`ListSubscribedWorkteamsResult`	`listSubscribedWorkteams(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest)` Gets a list of the work teams that you are subscribed to in the AWS Marketplace.
`ListTagsResult`	`listTags(ListTagsRequest listTagsRequest)` Returns the tags for the specified Amazon SageMaker resource.
`ListTrainingJobsResult`	`listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)` Lists training jobs.
`ListTrainingJobsForHyperParameterTuningJobResult`	`listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest)` Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
`ListTransformJobsResult`	`listTransformJobs(ListTransformJobsRequest listTransformJobsRequest)` Lists transform jobs.
`ListTrialComponentsResult`	`listTrialComponents(ListTrialComponentsRequest listTrialComponentsRequest)` Lists the trial components in your account.
`ListTrialsResult`	`listTrials(ListTrialsRequest listTrialsRequest)` Lists the trials in your account.
`ListUserProfilesResult`	`listUserProfiles(ListUserProfilesRequest listUserProfilesRequest)` Lists user profiles.
`ListWorkforcesResult`	`listWorkforces(ListWorkforcesRequest listWorkforcesRequest)` Use this operation to list all private and vendor workforces in an AWS Region.
`ListWorkteamsResult`	`listWorkteams(ListWorkteamsRequest listWorkteamsRequest)` Gets a list of private work teams that you have defined in a region.
`RenderUiTemplateResult`	`renderUiTemplate(RenderUiTemplateRequest renderUiTemplateRequest)` Renders the UI template so that you can preview the worker's experience.
`SearchResult`	`search(SearchRequest searchRequest)` Finds Amazon SageMaker resources that match a search query.
`void`	`shutdown()` Shuts down this client object, releasing any resources that might be held open.
`StartMonitoringScheduleResult`	`startMonitoringSchedule(StartMonitoringScheduleRequest startMonitoringScheduleRequest)` Starts a previously stopped monitoring schedule.
`StartNotebookInstanceResult`	`startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)` Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
`StopAutoMLJobResult`	`stopAutoMLJob(StopAutoMLJobRequest stopAutoMLJobRequest)` A method for forcing the termination of a running job.
`StopCompilationJobResult`	`stopCompilationJob(StopCompilationJobRequest stopCompilationJobRequest)` Stops a model compilation job.
`StopHyperParameterTuningJobResult`	`stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest)` Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
`StopLabelingJobResult`	`stopLabelingJob(StopLabelingJobRequest stopLabelingJobRequest)` Stops a running labeling job.
`StopMonitoringScheduleResult`	`stopMonitoringSchedule(StopMonitoringScheduleRequest stopMonitoringScheduleRequest)` Stops a previously started monitoring schedule.
`StopNotebookInstanceResult`	`stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)` Terminates the ML compute instance.
`StopProcessingJobResult`	`stopProcessingJob(StopProcessingJobRequest stopProcessingJobRequest)` Stops a processing job.
`StopTrainingJobResult`	`stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)` Stops a training job.
`StopTransformJobResult`	`stopTransformJob(StopTransformJobRequest stopTransformJobRequest)` Stops a transform job.
`UpdateAppImageConfigResult`	`updateAppImageConfig(UpdateAppImageConfigRequest updateAppImageConfigRequest)` Updates the properties of an AppImageConfig.
`UpdateCodeRepositoryResult`	`updateCodeRepository(UpdateCodeRepositoryRequest updateCodeRepositoryRequest)` Updates the specified Git repository with the specified values.
`UpdateDomainResult`	`updateDomain(UpdateDomainRequest updateDomainRequest)` Updates the default settings for new user profiles in the domain.
`UpdateEndpointResult`	`updateEndpoint(UpdateEndpointRequest updateEndpointRequest)` Deploys the new `EndpointConfig` specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous `EndpointConfig` (there is no availability loss).
`UpdateEndpointWeightsAndCapacitiesResult`	`updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)` Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
`UpdateExperimentResult`	`updateExperiment(UpdateExperimentRequest updateExperimentRequest)` Adds, updates, or removes the description of an experiment.
`UpdateImageResult`	`updateImage(UpdateImageRequest updateImageRequest)` Updates the properties of a SageMaker image.
`UpdateMonitoringScheduleResult`	`updateMonitoringSchedule(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest)` Updates a previously created schedule.
`UpdateNotebookInstanceResult`	`updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)` Updates a notebook instance.
`UpdateNotebookInstanceLifecycleConfigResult`	`updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest)` Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
`UpdateTrialResult`	`updateTrial(UpdateTrialRequest updateTrialRequest)` Updates the display name of a trial.
`UpdateTrialComponentResult`	`updateTrialComponent(UpdateTrialComponentRequest updateTrialComponentRequest)` Updates one or more properties of a trial component.
`UpdateUserProfileResult`	`updateUserProfile(UpdateUserProfileRequest updateUserProfileRequest)` Updates a user profile.
`UpdateWorkforceResult`	`updateWorkforce(UpdateWorkforceRequest updateWorkforceRequest)` Use this operation to update your workforce.
`UpdateWorkteamResult`	`updateWorkteam(UpdateWorkteamRequest updateWorkteamRequest)` Updates an existing work team with new member definitions or description.
`AmazonSageMakerWaiters`	`waiters()`

- Field Detail
  - ENDPOINT_PREFIX
```
static final String ENDPOINT_PREFIX
```
    The region metadata service name for computing region endpoints. You can use this value to retrieve metadata (such as supported regions) of the service.
    
    See Also:
    
    RegionUtils.getRegionsForService(String), Constant Field Values
- Method Detail
  - addTags
```
AddTagsResult addTags(AddTagsRequest addTagsRequest)
```
    Adds or overwrites one or more tags for the specified Amazon 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 AWS 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
    
    Parameters:
    
    addTagsRequest -
    
    Returns:
    
    Result of the AddTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - associateTrialComponent
```
AssociateTrialComponentResult associateTrialComponent(AssociateTrialComponentRequest associateTrialComponentRequest)
```
    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.
    
    Parameters:
    
    associateTrialComponentRequest -
    
    Returns:
    
    Result of the AssociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createAlgorithm
```
CreateAlgorithmResult createAlgorithm(CreateAlgorithmRequest createAlgorithmRequest)
```
    Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.
    
    Parameters:
    
    createAlgorithmRequest -
    
    Returns:
    
    Result of the CreateAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createApp
```
CreateAppResult createApp(CreateAppRequest createAppRequest)
```
    Creates a running App for the specified UserProfile. Supported Apps are JupyterServer and KernelGateway. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.
    
    Parameters:
    
    createAppRequest -
    
    Returns:
    
    Result of the CreateApp operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createAppImageConfig
```
CreateAppImageConfigResult createAppImageConfig(CreateAppImageConfigRequest createAppImageConfigRequest)
```
    Creates a configuration for running an Amazon SageMaker image as a KernelGateway app.
    
    Parameters:
    
    createAppImageConfigRequest -
    
    Returns:
    
    Result of the CreateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createAutoMLJob
```
CreateAutoMLJobResult createAutoMLJob(CreateAutoMLJobRequest createAutoMLJobRequest)
```
    Creates an Autopilot job.
    
    Find the best performing model after you run an Autopilot job by calling . Deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.
    
    For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.
    
    Parameters:
    
    createAutoMLJobRequest -
    
    Returns:
    
    Result of the CreateAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createCodeRepository
```
CreateCodeRepositoryResult createCodeRepository(CreateCodeRepositoryRequest createCodeRepositoryRequest)
```
    Creates a Git repository as a resource in your Amazon 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 Amazon 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 AWS CodeCommit or in any other Git repository.
    
    Parameters:
    
    createCodeRepositoryRequest -
    
    Returns:
    
    Result of the CreateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createCompilationJob
```
CreateCompilationJobResult createCompilationJob(CreateCompilationJobRequest createCompilationJobRequest)
```
    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 AWS 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.
    Parameters:
    
    createCompilationJobRequest -
    
    Returns:
    
    Result of the CreateCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createDomain
```
CreateDomainResult createDomain(CreateDomainRequest createDomainRequest)
```
    Creates a Domain used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. An AWS account is limited to one domain per region. Users within a domain can share notebook files and other artifacts with each other.
    
    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.
    
    VPC configuration
    
    All SageMaker Studio traffic between the domain and the EFS volume is through the specified VPC and subnets. For other Studio traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to Studio. 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 Studio 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 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.
    For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.
    Parameters:
    
    createDomainRequest -
    
    Returns:
    
    Result of the CreateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createEndpoint
```
CreateEndpointResult createEndpoint(CreateEndpointRequest createEndpointRequest)
```
    Creates an endpoint using the endpoint configuration specified in the request. Amazon 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 Amazon SageMaker hosting services.
    
    For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
    
    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 AWS Region in your AWS account.
    
    When it receives the request, Amazon 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 Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon 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, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS in an AWS Region in the AWS Identity and Access Management User Guide.
    
    Parameters:
    
    createEndpointRequest -
    
    Returns:
    
    Result of the CreateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createEndpointConfig
```
CreateEndpointConfigResult createEndpointConfig(CreateEndpointConfigRequest createEndpointConfigRequest)
```
    Creates an endpoint configuration that Amazon 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 Amazon SageMaker to provision. Then you call the CreateEndpoint API.
    
    Use this API if you want to use Amazon 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 Amazon 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. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.
    
    For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
    
    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.
    
    Parameters:
    
    createEndpointConfigRequest -
    
    Returns:
    
    Result of the CreateEndpointConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createExperiment
```
CreateExperimentResult createExperiment(CreateExperimentRequest createExperimentRequest)
```
    Creates an 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.
    
    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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
    
    Parameters:
    
    createExperimentRequest -
    
    Returns:
    
    Result of the CreateExperiment operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createFlowDefinition
```
CreateFlowDefinitionResult createFlowDefinition(CreateFlowDefinitionRequest createFlowDefinitionRequest)
```
    Creates a flow definition.
    
    Parameters:
    
    createFlowDefinitionRequest -
    
    Returns:
    
    Result of the CreateFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHumanTaskUi
```
CreateHumanTaskUiResult createHumanTaskUi(CreateHumanTaskUiRequest createHumanTaskUiRequest)
```
    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.
    
    Parameters:
    
    createHumanTaskUiRequest -
    
    Returns:
    
    Result of the CreateHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createHyperParameterTuningJob
```
CreateHyperParameterTuningJobResult createHyperParameterTuningJob(CreateHyperParameterTuningJobRequest createHyperParameterTuningJobRequest)
```
    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.
    
    Parameters:
    
    createHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the CreateHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImage
```
CreateImageResult createImage(CreateImageRequest createImageRequest)
```
    Creates a SageMaker Image. A SageMaker image represents a set of container images. Each of these container images is represented by a SageMaker ImageVersion.
    
    Parameters:
    
    createImageRequest -
    
    Returns:
    
    Result of the CreateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createImageVersion
```
CreateImageVersionResult createImageVersion(CreateImageVersionRequest createImageVersionRequest)
```
    Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Container Registry (ECR) container image specified by BaseImage.
    
    Parameters:
    
    createImageVersionRequest -
    
    Returns:
    
    Result of the CreateImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createLabelingJob
```
CreateLabelingJobResult createLabelingJob(CreateLabelingJobRequest createLabelingJobRequest)
```
    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 AWS 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.
    Parameters:
    
    createLabelingJobRequest -
    
    Returns:
    
    Result of the CreateLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModel
```
CreateModelResult createModel(CreateModelRequest createModelRequest)
```
    Creates a model in Amazon 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 Amazon 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. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.
    
    For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).
    
    To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.
    
    In the CreateModel request, you must define a container with the PrimaryContainer parameter.
    
    In the request, you also provide an IAM role that Amazon 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 AWS resources, you grant necessary permissions via this role.
    
    Parameters:
    
    createModelRequest -
    
    Returns:
    
    Result of the CreateModel operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createModelPackage
```
CreateModelPackageResult createModelPackage(CreateModelPackageRequest createModelPackageRequest)
```
    Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon 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 AWS Marketplace, provide a value for SourceAlgorithmSpecification.
    
    Parameters:
    
    createModelPackageRequest -
    
    Returns:
    
    Result of the CreateModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify an experiment, trial, or trial component.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createMonitoringSchedule
```
CreateMonitoringScheduleResult createMonitoringSchedule(CreateMonitoringScheduleRequest createMonitoringScheduleRequest)
```
    Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.
    
    Parameters:
    
    createMonitoringScheduleRequest -
    
    Returns:
    
    Result of the CreateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstance
```
CreateNotebookInstanceResult createNotebookInstance(CreateNotebookInstanceRequest createNotebookInstanceRequest)
```
    Creates an Amazon 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. Amazon 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.
    
    Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.
    
    After receiving the request, Amazon SageMaker does the following:
    1. Creates a network interface in the Amazon SageMaker VPC.
    2. (Option) If you specified SubnetId, Amazon 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, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.
    3. Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon 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, Amazon SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.
    
    After Amazon 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 Amazon SageMaker endpoints, and validate hosted models.
    
    For more information, see How It Works.
    Parameters:
    
    createNotebookInstanceRequest -
    
    Returns:
    
    Result of the CreateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createNotebookInstanceLifecycleConfig
```
CreateNotebookInstanceLifecycleConfigResult createNotebookInstanceLifecycleConfig(CreateNotebookInstanceLifecycleConfigRequest createNotebookInstanceLifecycleConfigRequest)
```
    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 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.
    
    Parameters:
    
    createNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the CreateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createPresignedDomainUrl
```
CreatePresignedDomainUrlResult createPresignedDomainUrl(CreatePresignedDomainUrlRequest createPresignedDomainUrlRequest)
```
    Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with the Domain's Amazon Elastic File System (EFS) volume. This operation can only be called when the authentication mode equals IAM.
    
    The URL that you get from a call to CreatePresignedDomainUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.
    
    Parameters:
    
    createPresignedDomainUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedDomainUrl operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createPresignedNotebookInstanceUrl
```
CreatePresignedNotebookInstanceUrlResult createPresignedNotebookInstanceUrl(CreatePresignedNotebookInstanceUrlRequest createPresignedNotebookInstanceUrlRequest)
```
    Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon 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 AWS console sign-in page.
    
    Parameters:
    
    createPresignedNotebookInstanceUrlRequest -
    
    Returns:
    
    Result of the CreatePresignedNotebookInstanceUrl operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createProcessingJob
```
CreateProcessingJobResult createProcessingJob(CreateProcessingJobRequest createProcessingJobRequest)
```
    Creates a processing job.
    
    Parameters:
    
    createProcessingJobRequest -
    
    Returns:
    
    Result of the CreateProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTrainingJob
```
CreateTrainingJobResult createTrainingJob(CreateTrainingJobRequest createTrainingJobRequest)
```
    Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location 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 in a machine learning service other than Amazon SageMaker, provided that you know how to use them for inferences.
    
    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 Amazon SageMaker, see Algorithms.
    - InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.
    - OutputDataConfig - Identifies the Amazon S3 bucket where you want Amazon 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 Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon 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 you are willing to wait for a managed spot training job to complete.
    For more information about Amazon SageMaker, see How It Works.
    Parameters:
    
    createTrainingJobRequest -
    
    Returns:
    
    Result of the CreateTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTransformJob
```
CreateTransformJobResult createTransformJob(CreateTransformJobRequest createTransformJobRequest)
```
    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 AWS Region in an AWS account.
    - ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS 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.
    Parameters:
    
    createTransformJobRequest -
    
    Returns:
    
    Result of the CreateTransformJob operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - createTrial
```
CreateTrialResult createTrial(CreateTrialRequest createTrialRequest)
```
    Creates an Amazon 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 Amazon SageMaker experiment.
    
    When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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.
    
    Parameters:
    
    createTrialRequest -
    
    Returns:
    
    Result of the CreateTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createTrialComponent
```
CreateTrialComponentResult createTrialComponent(CreateTrialComponentRequest createTrialComponentRequest)
```
    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 Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS 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 can only be invoked from within an Amazon SageMaker managed environment. This includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call to CreateTrialComponent from outside one of these environments results in an error.
    
    Parameters:
    
    createTrialComponentRequest -
    
    Returns:
    
    Result of the CreateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - createUserProfile
```
CreateUserProfileResult createUserProfile(CreateUserProfileRequest createUserProfileRequest)
```
    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 Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, 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 (EFS) home directory.
    
    Parameters:
    
    createUserProfileRequest -
    
    Returns:
    
    Result of the CreateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    See Also:
    
    AWS API Documentation
  - createWorkforce
```
CreateWorkforceResult createWorkforce(CreateWorkforceRequest createWorkforceRequest)
```
    Use this operation to create a workforce. This operation will return an error if a workforce already exists in the AWS Region that you specify. You can only create one workforce in each AWS Region per AWS account.
    
    If you want to create a new workforce in an AWS Region where a workforce already exists, use the 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).
    
    Parameters:
    
    createWorkforceRequest -
    
    Returns:
    
    Result of the CreateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - createWorkteam
```
CreateWorkteamResult createWorkteam(CreateWorkteamRequest createWorkteamRequest)
```
    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.
    
    Parameters:
    
    createWorkteamRequest -
    
    Returns:
    
    Result of the CreateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - deleteAlgorithm
```
DeleteAlgorithmResult deleteAlgorithm(DeleteAlgorithmRequest deleteAlgorithmRequest)
```
    Removes the specified algorithm from your account.
    
    Parameters:
    
    deleteAlgorithmRequest -
    
    Returns:
    
    Result of the DeleteAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteApp
```
DeleteAppResult deleteApp(DeleteAppRequest deleteAppRequest)
```
    Used to stop and delete an app.
    
    Parameters:
    
    deleteAppRequest -
    
    Returns:
    
    Result of the DeleteApp operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteAppImageConfig
```
DeleteAppImageConfigResult deleteAppImageConfig(DeleteAppImageConfigRequest deleteAppImageConfigRequest)
```
    Deletes an AppImageConfig.
    
    Parameters:
    
    deleteAppImageConfigRequest -
    
    Returns:
    
    Result of the DeleteAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteCodeRepository
```
DeleteCodeRepositoryResult deleteCodeRepository(DeleteCodeRepositoryRequest deleteCodeRepositoryRequest)
```
    Deletes the specified Git repository from your account.
    
    Parameters:
    
    deleteCodeRepositoryRequest -
    
    Returns:
    
    Result of the DeleteCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteDomain
```
DeleteDomainResult deleteDomain(DeleteDomainRequest deleteDomainRequest)
```
    Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using SSO. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.
    
    Parameters:
    
    deleteDomainRequest -
    
    Returns:
    
    Result of the DeleteDomain operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpoint
```
DeleteEndpointResult deleteEndpoint(DeleteEndpointRequest deleteEndpointRequest)
```
    Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.
    
    Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.
    
    Parameters:
    
    deleteEndpointRequest -
    
    Returns:
    
    Result of the DeleteEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteEndpointConfig
```
DeleteEndpointConfigResult deleteEndpointConfig(DeleteEndpointConfigRequest deleteEndpointConfigRequest)
```
    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.
    
    Parameters:
    
    deleteEndpointConfigRequest -
    
    Returns:
    
    Result of the DeleteEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteExperiment
```
DeleteExperimentResult deleteExperiment(DeleteExperimentRequest deleteExperimentRequest)
```
    Deletes an Amazon 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.
    
    Parameters:
    
    deleteExperimentRequest -
    
    Returns:
    
    Result of the DeleteExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteFlowDefinition
```
DeleteFlowDefinitionResult deleteFlowDefinition(DeleteFlowDefinitionRequest deleteFlowDefinitionRequest)
```
    Deletes the specified flow definition.
    
    Parameters:
    
    deleteFlowDefinitionRequest -
    
    Returns:
    
    Result of the DeleteFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteHumanTaskUi
```
DeleteHumanTaskUiResult deleteHumanTaskUi(DeleteHumanTaskUiRequest deleteHumanTaskUiRequest)
```
    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 . When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.
    
    Parameters:
    
    deleteHumanTaskUiRequest -
    
    Returns:
    
    Result of the DeleteHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteImage
```
DeleteImageResult deleteImage(DeleteImageRequest deleteImageRequest)
```
    Deletes a SageMaker image and all versions of the image. The container images aren't deleted.
    
    Parameters:
    
    deleteImageRequest -
    
    Returns:
    
    Result of the DeleteImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteImageVersion
```
DeleteImageVersionResult deleteImageVersion(DeleteImageVersionRequest deleteImageVersionRequest)
```
    Deletes a version of a SageMaker image. The container image the version represents isn't deleted.
    
    Parameters:
    
    deleteImageVersionRequest -
    
    Returns:
    
    Result of the DeleteImageVersion operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteModel
```
DeleteModelResult deleteModel(DeleteModelRequest deleteModelRequest)
```
    Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon 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.
    
    Parameters:
    
    deleteModelRequest -
    
    Returns:
    
    Result of the DeleteModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteModelPackage
```
DeleteModelPackageResult deleteModelPackage(DeleteModelPackageRequest deleteModelPackageRequest)
```
    Deletes a model package.
    
    A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.
    
    Parameters:
    
    deleteModelPackageRequest -
    
    Returns:
    
    Result of the DeleteModelPackage operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify an experiment, trial, or trial component.
    
    See Also:
    
    AWS API Documentation
  - deleteMonitoringSchedule
```
DeleteMonitoringScheduleResult deleteMonitoringSchedule(DeleteMonitoringScheduleRequest deleteMonitoringScheduleRequest)
```
    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.
    
    Parameters:
    
    deleteMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DeleteMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstance
```
DeleteNotebookInstanceResult deleteNotebookInstance(DeleteNotebookInstanceRequest deleteNotebookInstanceRequest)
```
    Deletes an Amazon 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. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.
    
    Parameters:
    
    deleteNotebookInstanceRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteNotebookInstanceLifecycleConfig
```
DeleteNotebookInstanceLifecycleConfigResult deleteNotebookInstanceLifecycleConfig(DeleteNotebookInstanceLifecycleConfigRequest deleteNotebookInstanceLifecycleConfigRequest)
```
    Deletes a notebook instance lifecycle configuration.
    
    Parameters:
    
    deleteNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DeleteNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTags
```
DeleteTagsResult deleteTags(DeleteTagsRequest deleteTagsRequest)
```
    Deletes the specified tags from an Amazon 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.
    
    Parameters:
    
    deleteTagsRequest -
    
    Returns:
    
    Result of the DeleteTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteTrial
```
DeleteTrialResult deleteTrial(DeleteTrialRequest deleteTrialRequest)
```
    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.
    
    Parameters:
    
    deleteTrialRequest -
    
    Returns:
    
    Result of the DeleteTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteTrialComponent
```
DeleteTrialComponentResult deleteTrialComponent(DeleteTrialComponentRequest deleteTrialComponentRequest)
```
    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.
    
    Parameters:
    
    deleteTrialComponentRequest -
    
    Returns:
    
    Result of the DeleteTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteUserProfile
```
DeleteUserProfileResult deleteUserProfile(DeleteUserProfileRequest deleteUserProfileRequest)
```
    Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.
    
    Parameters:
    
    deleteUserProfileRequest -
    
    Returns:
    
    Result of the DeleteUserProfile operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkforce
```
DeleteWorkforceResult deleteWorkforce(DeleteWorkforceRequest deleteWorkforceRequest)
```
    Use this operation to delete a workforce.
    
    If you want to create a new workforce in an AWS Region where a workforce already exists, use this operation to delete the existing workforce and then use to create a new workforce.
    
    If a private workforce contains one or more work teams, you must use the 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 recieve a ResourceInUse error.
    
    Parameters:
    
    deleteWorkforceRequest -
    
    Returns:
    
    Result of the DeleteWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - deleteWorkteam
```
DeleteWorkteamResult deleteWorkteam(DeleteWorkteamRequest deleteWorkteamRequest)
```
    Deletes an existing work team. This operation can't be undone.
    
    Parameters:
    
    deleteWorkteamRequest -
    
    Returns:
    
    Result of the DeleteWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - describeAlgorithm
```
DescribeAlgorithmResult describeAlgorithm(DescribeAlgorithmRequest describeAlgorithmRequest)
```
    Returns a description of the specified algorithm that is in your account.
    
    Parameters:
    
    describeAlgorithmRequest -
    
    Returns:
    
    Result of the DescribeAlgorithm operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeApp
```
DescribeAppResult describeApp(DescribeAppRequest describeAppRequest)
```
    Describes the app.
    
    Parameters:
    
    describeAppRequest -
    
    Returns:
    
    Result of the DescribeApp operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAppImageConfig
```
DescribeAppImageConfigResult describeAppImageConfig(DescribeAppImageConfigRequest describeAppImageConfigRequest)
```
    Describes an AppImageConfig.
    
    Parameters:
    
    describeAppImageConfigRequest -
    
    Returns:
    
    Result of the DescribeAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeAutoMLJob
```
DescribeAutoMLJobResult describeAutoMLJob(DescribeAutoMLJobRequest describeAutoMLJobRequest)
```
    Returns information about an Amazon SageMaker job.
    
    Parameters:
    
    describeAutoMLJobRequest -
    
    Returns:
    
    Result of the DescribeAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeCodeRepository
```
DescribeCodeRepositoryResult describeCodeRepository(DescribeCodeRepositoryRequest describeCodeRepositoryRequest)
```
    Gets details about the specified Git repository.
    
    Parameters:
    
    describeCodeRepositoryRequest -
    
    Returns:
    
    Result of the DescribeCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeCompilationJob
```
DescribeCompilationJobResult describeCompilationJob(DescribeCompilationJobRequest describeCompilationJobRequest)
```
    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.
    
    Parameters:
    
    describeCompilationJobRequest -
    
    Returns:
    
    Result of the DescribeCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeDomain
```
DescribeDomainResult describeDomain(DescribeDomainRequest describeDomainRequest)
```
    The description of the domain.
    
    Parameters:
    
    describeDomainRequest -
    
    Returns:
    
    Result of the DescribeDomain operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeEndpoint
```
DescribeEndpointResult describeEndpoint(DescribeEndpointRequest describeEndpointRequest)
```
    Returns the description of an endpoint.
    
    Parameters:
    
    describeEndpointRequest -
    
    Returns:
    
    Result of the DescribeEndpoint operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeEndpointConfig
```
DescribeEndpointConfigResult describeEndpointConfig(DescribeEndpointConfigRequest describeEndpointConfigRequest)
```
    Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
    
    Parameters:
    
    describeEndpointConfigRequest -
    
    Returns:
    
    Result of the DescribeEndpointConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeExperiment
```
DescribeExperimentResult describeExperiment(DescribeExperimentRequest describeExperimentRequest)
```
    Provides a list of an experiment's properties.
    
    Parameters:
    
    describeExperimentRequest -
    
    Returns:
    
    Result of the DescribeExperiment operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeFlowDefinition
```
DescribeFlowDefinitionResult describeFlowDefinition(DescribeFlowDefinitionRequest describeFlowDefinitionRequest)
```
    Returns information about the specified flow definition.
    
    Parameters:
    
    describeFlowDefinitionRequest -
    
    Returns:
    
    Result of the DescribeFlowDefinition operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHumanTaskUi
```
DescribeHumanTaskUiResult describeHumanTaskUi(DescribeHumanTaskUiRequest describeHumanTaskUiRequest)
```
    Returns information about the requested human task user interface (worker task template).
    
    Parameters:
    
    describeHumanTaskUiRequest -
    
    Returns:
    
    Result of the DescribeHumanTaskUi operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeHyperParameterTuningJob
```
DescribeHyperParameterTuningJobResult describeHyperParameterTuningJob(DescribeHyperParameterTuningJobRequest describeHyperParameterTuningJobRequest)
```
    Gets a description of a hyperparameter tuning job.
    
    Parameters:
    
    describeHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the DescribeHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImage
```
DescribeImageResult describeImage(DescribeImageRequest describeImageRequest)
```
    Describes a SageMaker image.
    
    Parameters:
    
    describeImageRequest -
    
    Returns:
    
    Result of the DescribeImage operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeImageVersion
```
DescribeImageVersionResult describeImageVersion(DescribeImageVersionRequest describeImageVersionRequest)
```
    Describes a version of a SageMaker image.
    
    Parameters:
    
    describeImageVersionRequest -
    
    Returns:
    
    Result of the DescribeImageVersion operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeLabelingJob
```
DescribeLabelingJobResult describeLabelingJob(DescribeLabelingJobRequest describeLabelingJobRequest)
```
    Gets information about a labeling job.
    
    Parameters:
    
    describeLabelingJobRequest -
    
    Returns:
    
    Result of the DescribeLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeModel
```
DescribeModelResult describeModel(DescribeModelRequest describeModelRequest)
```
    Describes a model that you created using the CreateModel API.
    
    Parameters:
    
    describeModelRequest -
    
    Returns:
    
    Result of the DescribeModel operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeModelPackage
```
DescribeModelPackageResult describeModelPackage(DescribeModelPackageRequest describeModelPackageRequest)
```
    Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.
    
    To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.
    
    Parameters:
    
    describeModelPackageRequest -
    
    Returns:
    
    Result of the DescribeModelPackage operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeMonitoringSchedule
```
DescribeMonitoringScheduleResult describeMonitoringSchedule(DescribeMonitoringScheduleRequest describeMonitoringScheduleRequest)
```
    Describes the schedule for a monitoring job.
    
    Parameters:
    
    describeMonitoringScheduleRequest -
    
    Returns:
    
    Result of the DescribeMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstance
```
DescribeNotebookInstanceResult describeNotebookInstance(DescribeNotebookInstanceRequest describeNotebookInstanceRequest)
```
    Returns information about a notebook instance.
    
    Parameters:
    
    describeNotebookInstanceRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeNotebookInstanceLifecycleConfig
```
DescribeNotebookInstanceLifecycleConfigResult describeNotebookInstanceLifecycleConfig(DescribeNotebookInstanceLifecycleConfigRequest describeNotebookInstanceLifecycleConfigRequest)
```
    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.
    
    Parameters:
    
    describeNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the DescribeNotebookInstanceLifecycleConfig operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeProcessingJob
```
DescribeProcessingJobResult describeProcessingJob(DescribeProcessingJobRequest describeProcessingJobRequest)
```
    Returns a description of a processing job.
    
    Parameters:
    
    describeProcessingJobRequest -
    
    Returns:
    
    Result of the DescribeProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeSubscribedWorkteam
```
DescribeSubscribedWorkteamResult describeSubscribedWorkteam(DescribeSubscribedWorkteamRequest describeSubscribedWorkteamRequest)
```
    Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.
    
    Parameters:
    
    describeSubscribedWorkteamRequest -
    
    Returns:
    
    Result of the DescribeSubscribedWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeTrainingJob
```
DescribeTrainingJobResult describeTrainingJob(DescribeTrainingJobRequest describeTrainingJobRequest)
```
    Returns information about a training job.
    
    Parameters:
    
    describeTrainingJobRequest -
    
    Returns:
    
    Result of the DescribeTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTransformJob
```
DescribeTransformJobResult describeTransformJob(DescribeTransformJobRequest describeTransformJobRequest)
```
    Returns information about a transform job.
    
    Parameters:
    
    describeTransformJobRequest -
    
    Returns:
    
    Result of the DescribeTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrial
```
DescribeTrialResult describeTrial(DescribeTrialRequest describeTrialRequest)
```
    Provides a list of a trial's properties.
    
    Parameters:
    
    describeTrialRequest -
    
    Returns:
    
    Result of the DescribeTrial operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeTrialComponent
```
DescribeTrialComponentResult describeTrialComponent(DescribeTrialComponentRequest describeTrialComponentRequest)
```
    Provides a list of a trials component's properties.
    
    Parameters:
    
    describeTrialComponentRequest -
    
    Returns:
    
    Result of the DescribeTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeUserProfile
```
DescribeUserProfileResult describeUserProfile(DescribeUserProfileRequest describeUserProfileRequest)
```
    Describes a user profile. For more information, see CreateUserProfile.
    
    Parameters:
    
    describeUserProfileRequest -
    
    Returns:
    
    Result of the DescribeUserProfile operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - describeWorkforce
```
DescribeWorkforceResult describeWorkforce(DescribeWorkforceRequest describeWorkforceRequest)
```
    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.
    
    Parameters:
    
    describeWorkforceRequest -
    
    Returns:
    
    Result of the DescribeWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - describeWorkteam
```
DescribeWorkteamResult describeWorkteam(DescribeWorkteamRequest describeWorkteamRequest)
```
    Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).
    
    Parameters:
    
    describeWorkteamRequest -
    
    Returns:
    
    Result of the DescribeWorkteam operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - disassociateTrialComponent
```
DisassociateTrialComponentResult disassociateTrialComponent(DisassociateTrialComponentRequest disassociateTrialComponentRequest)
```
    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.
    
    Parameters:
    
    disassociateTrialComponentRequest -
    
    Returns:
    
    Result of the DisassociateTrialComponent operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - getSearchSuggestions
```
GetSearchSuggestionsResult getSearchSuggestions(GetSearchSuggestionsRequest getSearchSuggestionsRequest)
```
    An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.
    
    Parameters:
    
    getSearchSuggestionsRequest -
    
    Returns:
    
    Result of the GetSearchSuggestions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAlgorithms
```
ListAlgorithmsResult listAlgorithms(ListAlgorithmsRequest listAlgorithmsRequest)
```
    Lists the machine learning algorithms that have been created.
    
    Parameters:
    
    listAlgorithmsRequest -
    
    Returns:
    
    Result of the ListAlgorithms operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAppImageConfigs
```
ListAppImageConfigsResult listAppImageConfigs(ListAppImageConfigsRequest listAppImageConfigsRequest)
```
    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.
    
    Parameters:
    
    listAppImageConfigsRequest -
    
    Returns:
    
    Result of the ListAppImageConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listApps
```
ListAppsResult listApps(ListAppsRequest listAppsRequest)
```
    Lists apps.
    
    Parameters:
    
    listAppsRequest -
    
    Returns:
    
    Result of the ListApps operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listAutoMLJobs
```
ListAutoMLJobsResult listAutoMLJobs(ListAutoMLJobsRequest listAutoMLJobsRequest)
```
    Request a list of jobs.
    
    Parameters:
    
    listAutoMLJobsRequest -
    
    Returns:
    
    Result of the ListAutoMLJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCandidatesForAutoMLJob
```
ListCandidatesForAutoMLJobResult listCandidatesForAutoMLJob(ListCandidatesForAutoMLJobRequest listCandidatesForAutoMLJobRequest)
```
    List the Candidates created for the job.
    
    Parameters:
    
    listCandidatesForAutoMLJobRequest -
    
    Returns:
    
    Result of the ListCandidatesForAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listCodeRepositories
```
ListCodeRepositoriesResult listCodeRepositories(ListCodeRepositoriesRequest listCodeRepositoriesRequest)
```
    Gets a list of the Git repositories in your account.
    
    Parameters:
    
    listCodeRepositoriesRequest -
    
    Returns:
    
    Result of the ListCodeRepositories operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listCompilationJobs
```
ListCompilationJobsResult listCompilationJobs(ListCompilationJobsRequest listCompilationJobsRequest)
```
    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.
    
    Parameters:
    
    listCompilationJobsRequest -
    
    Returns:
    
    Result of the ListCompilationJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listDomains
```
ListDomainsResult listDomains(ListDomainsRequest listDomainsRequest)
```
    Lists the domains.
    
    Parameters:
    
    listDomainsRequest -
    
    Returns:
    
    Result of the ListDomains operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpointConfigs
```
ListEndpointConfigsResult listEndpointConfigs(ListEndpointConfigsRequest listEndpointConfigsRequest)
```
    Lists endpoint configurations.
    
    Parameters:
    
    listEndpointConfigsRequest -
    
    Returns:
    
    Result of the ListEndpointConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listEndpoints
```
ListEndpointsResult listEndpoints(ListEndpointsRequest listEndpointsRequest)
```
    Lists endpoints.
    
    Parameters:
    
    listEndpointsRequest -
    
    Returns:
    
    Result of the ListEndpoints operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listExperiments
```
ListExperimentsResult listExperiments(ListExperimentsRequest listExperimentsRequest)
```
    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.
    
    Parameters:
    
    listExperimentsRequest -
    
    Returns:
    
    Result of the ListExperiments operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listFlowDefinitions
```
ListFlowDefinitionsResult listFlowDefinitions(ListFlowDefinitionsRequest listFlowDefinitionsRequest)
```
    Returns information about the flow definitions in your account.
    
    Parameters:
    
    listFlowDefinitionsRequest -
    
    Returns:
    
    Result of the ListFlowDefinitions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHumanTaskUis
```
ListHumanTaskUisResult listHumanTaskUis(ListHumanTaskUisRequest listHumanTaskUisRequest)
```
    Returns information about the human task user interfaces in your account.
    
    Parameters:
    
    listHumanTaskUisRequest -
    
    Returns:
    
    Result of the ListHumanTaskUis operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listHyperParameterTuningJobs
```
ListHyperParameterTuningJobsResult listHyperParameterTuningJobs(ListHyperParameterTuningJobsRequest listHyperParameterTuningJobsRequest)
```
    Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
    
    Parameters:
    
    listHyperParameterTuningJobsRequest -
    
    Returns:
    
    Result of the ListHyperParameterTuningJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listImageVersions
```
ListImageVersionsResult listImageVersions(ListImageVersionsRequest listImageVersionsRequest)
```
    Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.
    
    Parameters:
    
    listImageVersionsRequest -
    
    Returns:
    
    Result of the ListImageVersions operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listImages
```
ListImagesResult listImages(ListImagesRequest listImagesRequest)
```
    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.
    
    Parameters:
    
    listImagesRequest -
    
    Returns:
    
    Result of the ListImages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobs
```
ListLabelingJobsResult listLabelingJobs(ListLabelingJobsRequest listLabelingJobsRequest)
```
    Gets a list of labeling jobs.
    
    Parameters:
    
    listLabelingJobsRequest -
    
    Returns:
    
    Result of the ListLabelingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listLabelingJobsForWorkteam
```
ListLabelingJobsForWorkteamResult listLabelingJobsForWorkteam(ListLabelingJobsForWorkteamRequest listLabelingJobsForWorkteamRequest)
```
    Gets a list of labeling jobs assigned to a specified work team.
    
    Parameters:
    
    listLabelingJobsForWorkteamRequest -
    
    Returns:
    
    Result of the ListLabelingJobsForWorkteam operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listModelPackages
```
ListModelPackagesResult listModelPackages(ListModelPackagesRequest listModelPackagesRequest)
```
    Lists the model packages that have been created.
    
    Parameters:
    
    listModelPackagesRequest -
    
    Returns:
    
    Result of the ListModelPackages operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listModels
```
ListModelsResult listModels(ListModelsRequest listModelsRequest)
```
    Lists models created with the CreateModel API.
    
    Parameters:
    
    listModelsRequest -
    
    Returns:
    
    Result of the ListModels operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringExecutions
```
ListMonitoringExecutionsResult listMonitoringExecutions(ListMonitoringExecutionsRequest listMonitoringExecutionsRequest)
```
    Returns list of all monitoring job executions.
    
    Parameters:
    
    listMonitoringExecutionsRequest -
    
    Returns:
    
    Result of the ListMonitoringExecutions operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listMonitoringSchedules
```
ListMonitoringSchedulesResult listMonitoringSchedules(ListMonitoringSchedulesRequest listMonitoringSchedulesRequest)
```
    Returns list of all monitoring schedules.
    
    Parameters:
    
    listMonitoringSchedulesRequest -
    
    Returns:
    
    Result of the ListMonitoringSchedules operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstanceLifecycleConfigs
```
ListNotebookInstanceLifecycleConfigsResult listNotebookInstanceLifecycleConfigs(ListNotebookInstanceLifecycleConfigsRequest listNotebookInstanceLifecycleConfigsRequest)
```
    Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
    
    Parameters:
    
    listNotebookInstanceLifecycleConfigsRequest -
    
    Returns:
    
    Result of the ListNotebookInstanceLifecycleConfigs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listNotebookInstances
```
ListNotebookInstancesResult listNotebookInstances(ListNotebookInstancesRequest listNotebookInstancesRequest)
```
    Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
    
    Parameters:
    
    listNotebookInstancesRequest -
    
    Returns:
    
    Result of the ListNotebookInstances operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listProcessingJobs
```
ListProcessingJobsResult listProcessingJobs(ListProcessingJobsRequest listProcessingJobsRequest)
```
    Lists processing jobs that satisfy various filters.
    
    Parameters:
    
    listProcessingJobsRequest -
    
    Returns:
    
    Result of the ListProcessingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listSubscribedWorkteams
```
ListSubscribedWorkteamsResult listSubscribedWorkteams(ListSubscribedWorkteamsRequest listSubscribedWorkteamsRequest)
```
    Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.
    
    Parameters:
    
    listSubscribedWorkteamsRequest -
    
    Returns:
    
    Result of the ListSubscribedWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTags
```
ListTagsResult listTags(ListTagsRequest listTagsRequest)
```
    Returns the tags for the specified Amazon SageMaker resource.
    
    Parameters:
    
    listTagsRequest -
    
    Returns:
    
    Result of the ListTags operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobs
```
ListTrainingJobsResult listTrainingJobs(ListTrainingJobsRequest listTrainingJobsRequest)
```
    Lists training jobs.
    
    Parameters:
    
    listTrainingJobsRequest -
    
    Returns:
    
    Result of the ListTrainingJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrainingJobsForHyperParameterTuningJob
```
ListTrainingJobsForHyperParameterTuningJobResult listTrainingJobsForHyperParameterTuningJob(ListTrainingJobsForHyperParameterTuningJobRequest listTrainingJobsForHyperParameterTuningJobRequest)
```
    Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
    
    Parameters:
    
    listTrainingJobsForHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the ListTrainingJobsForHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTransformJobs
```
ListTransformJobsResult listTransformJobs(ListTransformJobsRequest listTransformJobsRequest)
```
    Lists transform jobs.
    
    Parameters:
    
    listTransformJobsRequest -
    
    Returns:
    
    Result of the ListTransformJobs operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listTrialComponents
```
ListTrialComponentsResult listTrialComponents(ListTrialComponentsRequest listTrialComponentsRequest)
```
    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
    Parameters:
    
    listTrialComponentsRequest -
    
    Returns:
    
    Result of the ListTrialComponents operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listTrials
```
ListTrialsResult listTrials(ListTrialsRequest listTrialsRequest)
```
    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.
    
    Parameters:
    
    listTrialsRequest -
    
    Returns:
    
    Result of the ListTrials operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - listUserProfiles
```
ListUserProfilesResult listUserProfiles(ListUserProfilesRequest listUserProfilesRequest)
```
    Lists user profiles.
    
    Parameters:
    
    listUserProfilesRequest -
    
    Returns:
    
    Result of the ListUserProfiles operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkforces
```
ListWorkforcesResult listWorkforces(ListWorkforcesRequest listWorkforcesRequest)
```
    Use this operation to list all private and vendor workforces in an AWS Region. Note that you can only have one private workforce per AWS Region.
    
    Parameters:
    
    listWorkforcesRequest -
    
    Returns:
    
    Result of the ListWorkforces operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - listWorkteams
```
ListWorkteamsResult listWorkteams(ListWorkteamsRequest listWorkteamsRequest)
```
    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.
    
    Parameters:
    
    listWorkteamsRequest -
    
    Returns:
    
    Result of the ListWorkteams operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - renderUiTemplate
```
RenderUiTemplateResult renderUiTemplate(RenderUiTemplateRequest renderUiTemplateRequest)
```
    Renders the UI template so that you can preview the worker's experience.
    
    Parameters:
    
    renderUiTemplateRequest -
    
    Returns:
    
    Result of the RenderUiTemplate operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - search
```
SearchResult search(SearchRequest searchRequest)
```
    Finds Amazon 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.
    
    Parameters:
    
    searchRequest -
    
    Returns:
    
    Result of the Search operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - startMonitoringSchedule
```
StartMonitoringScheduleResult startMonitoringSchedule(StartMonitoringScheduleRequest startMonitoringScheduleRequest)
```
    Starts a previously stopped monitoring schedule.
    
    New monitoring schedules are immediately started after creation.
    
    Parameters:
    
    startMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StartMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - startNotebookInstance
```
StartNotebookInstanceResult startNotebookInstance(StartNotebookInstanceRequest startNotebookInstanceRequest)
```
    Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.
    
    Parameters:
    
    startNotebookInstanceRequest -
    
    Returns:
    
    Result of the StartNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - stopAutoMLJob
```
StopAutoMLJobResult stopAutoMLJob(StopAutoMLJobRequest stopAutoMLJobRequest)
```
    A method for forcing the termination of a running job.
    
    Parameters:
    
    stopAutoMLJobRequest -
    
    Returns:
    
    Result of the StopAutoMLJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopCompilationJob
```
StopCompilationJobResult stopCompilationJob(StopCompilationJobRequest stopCompilationJobRequest)
```
    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 CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.
    
    Parameters:
    
    stopCompilationJobRequest -
    
    Returns:
    
    Result of the StopCompilationJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopHyperParameterTuningJob
```
StopHyperParameterTuningJobResult stopHyperParameterTuningJob(StopHyperParameterTuningJobRequest stopHyperParameterTuningJobRequest)
```
    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.
    
    Parameters:
    
    stopHyperParameterTuningJobRequest -
    
    Returns:
    
    Result of the StopHyperParameterTuningJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopLabelingJob
```
StopLabelingJobResult stopLabelingJob(StopLabelingJobRequest stopLabelingJobRequest)
```
    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.
    
    Parameters:
    
    stopLabelingJobRequest -
    
    Returns:
    
    Result of the StopLabelingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopMonitoringSchedule
```
StopMonitoringScheduleResult stopMonitoringSchedule(StopMonitoringScheduleRequest stopMonitoringScheduleRequest)
```
    Stops a previously started monitoring schedule.
    
    Parameters:
    
    stopMonitoringScheduleRequest -
    
    Returns:
    
    Result of the StopMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopNotebookInstance
```
StopNotebookInstanceResult stopNotebookInstance(StopNotebookInstanceRequest stopNotebookInstanceRequest)
```
    Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume. Amazon 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.
    
    Parameters:
    
    stopNotebookInstanceRequest -
    
    Returns:
    
    Result of the StopNotebookInstance operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - stopProcessingJob
```
StopProcessingJobResult stopProcessingJob(StopProcessingJobRequest stopProcessingJobRequest)
```
    Stops a processing job.
    
    Parameters:
    
    stopProcessingJobRequest -
    
    Returns:
    
    Result of the StopProcessingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTrainingJob
```
StopTrainingJobResult stopTrainingJob(StopTrainingJobRequest stopTrainingJobRequest)
```
    Stops a training job. To stop a job, Amazon 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, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.
    
    Parameters:
    
    stopTrainingJobRequest -
    
    Returns:
    
    Result of the StopTrainingJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - stopTransformJob
```
StopTransformJobResult stopTransformJob(StopTransformJobRequest stopTransformJobRequest)
```
    Stops a 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 transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.
    
    Parameters:
    
    stopTransformJobRequest -
    
    Returns:
    
    Result of the StopTransformJob operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateAppImageConfig
```
UpdateAppImageConfigResult updateAppImageConfig(UpdateAppImageConfigRequest updateAppImageConfigRequest)
```
    Updates the properties of an AppImageConfig.
    
    Parameters:
    
    updateAppImageConfigRequest -
    
    Returns:
    
    Result of the UpdateAppImageConfig operation returned by the service.
    
    Throws:
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateCodeRepository
```
UpdateCodeRepositoryResult updateCodeRepository(UpdateCodeRepositoryRequest updateCodeRepositoryRequest)
```
    Updates the specified Git repository with the specified values.
    
    Parameters:
    
    updateCodeRepositoryRequest -
    
    Returns:
    
    Result of the UpdateCodeRepository operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateDomain
```
UpdateDomainResult updateDomain(UpdateDomainRequest updateDomainRequest)
```
    Updates the default settings for new user profiles in the domain.
    
    Parameters:
    
    updateDomainRequest -
    
    Returns:
    
    Result of the UpdateDomain operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateEndpoint
```
UpdateEndpointResult updateEndpoint(UpdateEndpointRequest updateEndpointRequest)
```
    Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
    
    When Amazon 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.
    
    Parameters:
    
    updateEndpointRequest -
    
    Returns:
    
    Result of the UpdateEndpoint operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateEndpointWeightsAndCapacities
```
UpdateEndpointWeightsAndCapacitiesResult updateEndpointWeightsAndCapacities(UpdateEndpointWeightsAndCapacitiesRequest updateEndpointWeightsAndCapacitiesRequest)
```
    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, Amazon 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.
    
    Parameters:
    
    updateEndpointWeightsAndCapacitiesRequest -
    
    Returns:
    
    Result of the UpdateEndpointWeightsAndCapacities operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateExperiment
```
UpdateExperimentResult updateExperiment(UpdateExperimentRequest updateExperimentRequest)
```
    Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.
    
    Parameters:
    
    updateExperimentRequest -
    
    Returns:
    
    Result of the UpdateExperiment operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify an experiment, trial, or trial component.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateImage
```
UpdateImageResult updateImage(UpdateImageRequest updateImageRequest)
```
    Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.
    
    Parameters:
    
    updateImageRequest -
    
    Returns:
    
    Result of the UpdateImage operation returned by the service.
    
    Throws:
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateMonitoringSchedule
```
UpdateMonitoringScheduleResult updateMonitoringSchedule(UpdateMonitoringScheduleRequest updateMonitoringScheduleRequest)
```
    Updates a previously created schedule.
    
    Parameters:
    
    updateMonitoringScheduleRequest -
    
    Returns:
    
    Result of the UpdateMonitoringSchedule operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstance
```
UpdateNotebookInstanceResult updateNotebookInstance(UpdateNotebookInstanceRequest updateNotebookInstanceRequest)
```
    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.
    
    Parameters:
    
    updateNotebookInstanceRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstance operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateNotebookInstanceLifecycleConfig
```
UpdateNotebookInstanceLifecycleConfigResult updateNotebookInstanceLifecycleConfig(UpdateNotebookInstanceLifecycleConfigRequest updateNotebookInstanceLifecycleConfigRequest)
```
    Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.
    
    Parameters:
    
    updateNotebookInstanceLifecycleConfigRequest -
    
    Returns:
    
    Result of the UpdateNotebookInstanceLifecycleConfig operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - updateTrial
```
UpdateTrialResult updateTrial(UpdateTrialRequest updateTrialRequest)
```
    Updates the display name of a trial.
    
    Parameters:
    
    updateTrialRequest -
    
    Returns:
    
    Result of the UpdateTrial operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify an experiment, trial, or trial component.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateTrialComponent
```
UpdateTrialComponentResult updateTrialComponent(UpdateTrialComponentRequest updateTrialComponentRequest)
```
    Updates one or more properties of a trial component.
    
    Parameters:
    
    updateTrialComponentRequest -
    
    Returns:
    
    Result of the UpdateTrialComponent operation returned by the service.
    
    Throws:
    
    ConflictException - There was a conflict when you attempted to modify an experiment, trial, or trial component.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateUserProfile
```
UpdateUserProfileResult updateUserProfile(UpdateUserProfileRequest updateUserProfileRequest)
```
    Updates a user profile.
    
    Parameters:
    
    updateUserProfileRequest -
    
    Returns:
    
    Result of the UpdateUserProfile operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    ResourceInUseException - Resource being accessed is in use.
    
    ResourceNotFoundException - Resource being access is not found.
    
    See Also:
    
    AWS API Documentation
  - updateWorkforce
```
UpdateWorkforceResult updateWorkforce(UpdateWorkforceRequest updateWorkforceRequest)
```
    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.
    
    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.
    
    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 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 operation.
    
    This operation only applies to private workforces.
    
    Parameters:
    
    updateWorkforceRequest -
    
    Returns:
    
    Result of the UpdateWorkforce operation returned by the service.
    
    See Also:
    
    AWS API Documentation
  - updateWorkteam
```
UpdateWorkteamResult updateWorkteam(UpdateWorkteamRequest updateWorkteamRequest)
```
    Updates an existing work team with new member definitions or description.
    
    Parameters:
    
    updateWorkteamRequest -
    
    Returns:
    
    Result of the UpdateWorkteam operation returned by the service.
    
    Throws:
    
    ResourceLimitExceededException - You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.
    
    See Also:
    
    AWS API Documentation
  - shutdown
```
void shutdown()
```
    Shuts down this client object, releasing any resources that might be held open. This is an optional method, and callers are not expected to call it, but can if they want to explicitly release any open resources. Once a client has been shutdown, it should not be used to make any more requests.
  - getCachedResponseMetadata
```
ResponseMetadata getCachedResponseMetadata(AmazonWebServiceRequest request)
```
    Returns additional metadata for a previously executed successful request, typically used for debugging issues where a service isn't acting as expected. This data isn't considered part of the result data returned by an operation, so it's available through this separate, diagnostic interface.
    Response metadata is only cached for a limited period of time, so if you need to access this extra diagnostic information for an executed request, you should use this method to retrieve it as soon as possible after executing a request.
    
    Parameters:
    
    request - The originally executed request.
    
    Returns:
    
    The response metadata for the specified request, or null if none is available.
  - waiters
```
AmazonSageMakerWaiters waiters()
```

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Interface AmazonSageMaker

Field Summary

Method Summary

Field Detail

ENDPOINT_PREFIX

Method Detail

addTags

associateTrialComponent

createAlgorithm

createApp

createAppImageConfig

createAutoMLJob

createCodeRepository

createCompilationJob

createDomain

createEndpoint

createEndpointConfig

createExperiment

createFlowDefinition

createHumanTaskUi

createHyperParameterTuningJob

createImage

createImageVersion

createLabelingJob

createModel

createModelPackage

createMonitoringSchedule

createNotebookInstance

createNotebookInstanceLifecycleConfig

createPresignedDomainUrl

createPresignedNotebookInstanceUrl

createProcessingJob

createTrainingJob

createTransformJob

createTrial

createTrialComponent

createUserProfile

createWorkforce

createWorkteam

deleteAlgorithm

deleteApp

deleteAppImageConfig

deleteCodeRepository

deleteDomain

deleteEndpoint

deleteEndpointConfig

deleteExperiment

deleteFlowDefinition

deleteHumanTaskUi

deleteImage

deleteImageVersion

deleteModel

deleteModelPackage

deleteMonitoringSchedule

deleteNotebookInstance

deleteNotebookInstanceLifecycleConfig

deleteTags

deleteTrial

deleteTrialComponent

deleteUserProfile

deleteWorkforce

deleteWorkteam

describeAlgorithm

describeApp

describeAppImageConfig

describeAutoMLJob

describeCodeRepository

describeCompilationJob

describeDomain

describeEndpoint

describeEndpointConfig

describeExperiment

describeFlowDefinition

describeHumanTaskUi

describeHyperParameterTuningJob

describeImage

describeImageVersion

describeLabelingJob

describeModel