amazonaws.com – sagemaker

Tools (304)

Extracted live via the executor SDK.

• xAmzTargetSageMakerAddAssociation.addAssociation

  Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

• xAmzTargetSageMakerAddTags.addTags

  Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints.

  Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see For more information, see Amazon Web Services Tagging Strategies.

  Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob

  Tags that you add to a SageMaker Studio Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

• xAmzTargetSageMakerAssociateTrialComponent.associateTrialComponent

  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.

• xAmzTargetSageMakerBatchDescribeModelPackage.batchDescribeModelPackage

  This action batch describes a list of versioned model packages

• xAmzTargetSageMakerCreateAction.createAction

  Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

• xAmzTargetSageMakerCreateAlgorithm.createAlgorithm

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

• xAmzTargetSageMakerCreateApp.createApp

  Creates a running app for the specified UserProfile. 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.

• xAmzTargetSageMakerCreateAppImageConfig.createAppImageConfig

  Creates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on the image, and a list of the kernels in the image.

• xAmzTargetSageMakerCreateArtifact.createArtifact

  Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

• xAmzTargetSageMakerCreateAutoMlJob.createAutoMlJob

  Creates an Autopilot job.

  Find the best-performing model after you run an Autopilot job by calling DescribeAutoMLJob.

  For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

• xAmzTargetSageMakerCreateAutoMlJobV2.createAutoMlJobV2

  Creates an Amazon SageMaker AutoML job that uses non-tabular data such as images or text for Computer Vision or Natural Language Processing problems.

  Find the resulting model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

  To create an AutoMLJob using tabular data, see CreateAutoMLJob.

  This API action is callable through SageMaker Canvas only. Calling it directly from the CLI or an SDK results in an error.

• xAmzTargetSageMakerCreateCodeRepository.createCodeRepository

  Creates a Git repository as a resource in your SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

  The repository can be hosted either in Amazon Web Services CodeCommit or in any other Git repository.

• xAmzTargetSageMakerCreateCompilationJob.createCompilationJob

  Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket that you specify.

  If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services IoT Greengrass. In that case, deploy them as an ML resource.

  In the request body, you provide the following:

  • A name for the compilation job

  • Information about the input model artifacts

  • The output location for the compiled model and the device (target) that the model runs on

  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job.

  You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

  To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

• xAmzTargetSageMakerCreateContext.createContext

  Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

• xAmzTargetSageMakerCreateDataQualityJobDefinition.createDataQualityJobDefinition

  Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

• xAmzTargetSageMakerCreateDeviceFleet.createDeviceFleet

  Creates a device fleet.

• xAmzTargetSageMakerCreateDomain.createDomain

  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. Users within a domain can share notebook files and other artifacts with each other.

  EFS storage

  When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

  SageMaker uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.

  VPC configuration

  All 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.

  NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a SageMaker Studio app successfully.

  For more information, see Connect SageMaker Studio Notebooks to Resources in a VPC.

• xAmzTargetSageMakerCreateEdgeDeploymentPlan.createEdgeDeploymentPlan

  Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

• xAmzTargetSageMakerCreateEdgeDeploymentStage.createEdgeDeploymentStage

  Creates a new stage in an existing edge deployment plan.

• xAmzTargetSageMakerCreateEdgePackagingJob.createEdgePackagingJob

  Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

• xAmzTargetSageMakerCreateEndpoint.createEndpoint

  Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

  Use this API to deploy models using SageMaker hosting services.

  For an example that calls this method when deploying a model to SageMaker hosting services, see the Create Endpoint example notebook.

  You must not delete an EndpointConfig that is in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

  The endpoint name must be unique within an Amazon Web Services Region in your Amazon Web Services account.

  When it receives the request, SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

  When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

  When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

  If any of the models hosted at this endpoint get model data from an Amazon S3 location, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

  To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

    "Resource": [

    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"

    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

    ]

    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

• xAmzTargetSageMakerCreateEndpointConfig.createEndpointConfig

  Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want SageMaker to provision. Then you call the CreateEndpoint API.

  Use this API if you want to use SageMaker hosting services to deploy models into production.

  In the request, you define a ProductionVariant, for each model that you want to deploy. Each ProductionVariant parameter also describes the resources that you want SageMaker to provision. This includes the number and type of ML compute instances to deploy.

  If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

  When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

• xAmzTargetSageMakerCreateExperiment.createExperiment

  Creates a SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.

  In the Studio UI, trials are referred to as run groups and trial components are referred to as runs.

  The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

  When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

  You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.

  To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.

  To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

• xAmzTargetSageMakerCreateFeatureGroup.createFeatureGroup

  Create a new FeatureGroup. A FeatureGroup is a group of Features defined in the FeatureStore to describe a Record.

  The FeatureGroup defines the schema and features contained in the FeatureGroup. A FeatureGroup definition is composed of a list of Features, a RecordIdentifierFeatureName, an EventTimeFeatureName and configurations for its OnlineStore and OfflineStore. Check Amazon Web Services service quotas to see the FeatureGroups quota for your Amazon Web Services account.

  You must include at least one of OnlineStoreConfig and OfflineStoreConfig to create a FeatureGroup.

• xAmzTargetSageMakerCreateFlowDefinition.createFlowDefinition

  Creates a flow definition.

• xAmzTargetSageMakerCreateHub.createHub

  Create a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerCreateHumanTaskUi.createHumanTaskUi

  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.

• xAmzTargetSageMakerCreateHyperParameterTuningJob.createHyperParameterTuningJob

  Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

  A hyperparameter tuning job automatically creates Amazon SageMaker experiments, trials, and trial components for each training job that it runs. You can view these entities in Amazon SageMaker Studio. For more information, see View Experiments, Trials, and Trial Components.

  Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

• xAmzTargetSageMakerCreateImage.createImage

  Creates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Container Registry (ECR). For more information, see Bring your own SageMaker image.

• xAmzTargetSageMakerCreateImageVersion.createImageVersion

  Creates a version of the SageMaker image specified by ImageName. The version represents the Amazon Elastic Container Registry (ECR) container image specified by BaseImage.

• xAmzTargetSageMakerCreateInferenceExperiment.createInferenceExperiment

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

  Use this API to setup and schedule an experiment to compare model variants on a Amazon SageMaker inference endpoint. For more information about inference experiments, see Shadow tests.

  Amazon SageMaker begins your experiment at the scheduled time and routes traffic to your endpoint's model variants based on your specified configuration.

  While the experiment is in progress or after it has concluded, you can view metrics that compare your model variants. For more information, see View, monitor, and edit shadow tests.

• xAmzTargetSageMakerCreateInferenceRecommendationsJob.createInferenceRecommendationsJob

  Starts a recommendation job. You can create either an instance recommendation or load test job.

• xAmzTargetSageMakerCreateLabelingJob.createLabelingJob

  Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

  You can select your workforce from one of three providers:

  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.

  • One or more vendors that you select from the Amazon Web Services Marketplace. Vendors provide expertise in specific areas.

  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

  You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.

  The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.

  The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

  You can use this operation to create a static labeling job or a streaming labeling job. A static labeling job stops if all data objects in the input manifest file identified in ManifestS3Uri have been labeled. A streaming labeling job runs perpetually until it is manually stopped, or remains idle for 10 days. You can send new data objects to an active (InProgress) streaming labeling job in real time. To learn how to create a static labeling job, see Create a Labeling Job (API) in the Amazon SageMaker Developer Guide. To learn how to create a streaming labeling job, see Create a Streaming Labeling Job.

• xAmzTargetSageMakerCreateModel.createModel

  Creates a model in SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

  Use this API to create a model if you want to use SageMaker hosting services or run a batch transform job.

  To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

  For an example that calls this method when deploying a model to SageMaker hosting services, see Create a Model (Amazon Web Services SDK for Python (Boto 3)).

  To run a batch transform using your model, you start a job with the CreateTransformJob API. SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

  In the request, you also provide an IAM role that SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

• xAmzTargetSageMakerCreateModelBiasJobDefinition.createModelBiasJobDefinition

  Creates the definition for a model bias job.

• xAmzTargetSageMakerCreateModelCard.createModelCard

  Creates an Amazon SageMaker Model Card.

  For information about how to use model cards, see Amazon SageMaker Model Card.

• xAmzTargetSageMakerCreateModelCardExportJob.createModelCardExportJob

  Creates an Amazon SageMaker Model Card export job.

• xAmzTargetSageMakerCreateModelExplainabilityJobDefinition.createModelExplainabilityJobDefinition

  Creates the definition for a model explainability job.

• xAmzTargetSageMakerCreateModelPackage.createModelPackage

  Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

  To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

  There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

• xAmzTargetSageMakerCreateModelPackageGroup.createModelPackageGroup

  Creates a model group. A model group contains a group of model versions.

• xAmzTargetSageMakerCreateModelQualityJobDefinition.createModelQualityJobDefinition

  Creates a definition for a job that monitors model quality and drift. For information about model monitor, see Amazon SageMaker Model Monitor.

• xAmzTargetSageMakerCreateMonitoringSchedule.createMonitoringSchedule

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

• xAmzTargetSageMakerCreateNotebookInstance.createNotebookInstance

  Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

  In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

  SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use SageMaker with a specific algorithm or with a machine learning framework.

  After receiving the request, SageMaker does the following:

  1. Creates a network interface in the SageMaker VPC.

  2. (Option) If you specified SubnetId, SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.

  3. Launches an EC2 instance of the type specified in the request in the SageMaker VPC. If you specified SubnetId of your VPC, SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

  After creating the notebook instance, SageMaker returns its Amazon Resource Name (ARN). You can't change the name of a notebook instance after you create it.

  After SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating SageMaker endpoints, and validate hosted models.

  For more information, see How It Works.

• xAmzTargetSageMakerCreateNotebookInstanceLifecycleConfig.createNotebookInstanceLifecycleConfig

  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.

• xAmzTargetSageMakerCreatePipeline.createPipeline

  Creates a pipeline using a JSON pipeline definition.

• xAmzTargetSageMakerCreatePresignedDomainUrl.createPresignedDomainUrl

  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 IAM role or user passed to this API defines the permissions to access the app. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the app.

  You can restrict access to this API and to the URL that it returns to a list of IP addresses, Amazon VPCs or Amazon VPC Endpoints that you specify. For more information, see Connect to SageMaker Studio Through an Interface VPC Endpoint .

  The URL that you get from a call to CreatePresignedDomainUrl has a default timeout of 5 minutes. You can configure this value using ExpiresInSeconds. If you try to use the URL after the timeout limit expires, you are directed to the Amazon Web Services console sign-in page.

• xAmzTargetSageMakerCreatePresignedNotebookInstanceUrl.createPresignedNotebookInstanceUrl

  Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose Open next to a notebook instance, SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

  The IAM role or user used to call this API defines the permissions to access the notebook instance. Once the presigned URL is created, no additional permission is required to access this URL. IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.

  You can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

  The URL that you get from a call to CreatePresignedNotebookInstanceUrl is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the Amazon Web Services console sign-in page.

• xAmzTargetSageMakerCreateProcessingJob.createProcessingJob

  Creates a processing job.

• xAmzTargetSageMakerCreateProject.createProject

  Creates a machine learning (ML) project that can contain one or more templates that set up an ML pipeline from training to deploying an approved model.

• xAmzTargetSageMakerCreateSpace.createSpace

  Creates a space used for real time collaboration in a Domain.

• xAmzTargetSageMakerCreateStudioLifecycleConfig.createStudioLifecycleConfig

  Creates a new Studio Lifecycle Configuration.

• xAmzTargetSageMakerCreateTrainingJob.createTrainingJob

  Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

  If you choose to host your model using SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a machine learning service other than SageMaker, provided that you know how to use them for inference.

  In the request body, you provide the following:

  • AlgorithmSpecification - Identifies the training algorithm to use.

  • HyperParameters - Specify these algorithm-specific parameters to enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms.

    Do not include any security-sensitive information including account access IDs, secrets or tokens in any hyperparameter field. If the use of security-sensitive credentials are detected, SageMaker will reject your training job request and return an exception error.

  • InputDataConfig - Describes the input required by the training job and the Amazon S3, EFS, or FSx location where it is stored.

  • OutputDataConfig - Identifies the Amazon S3 bucket where you want SageMaker to save the results of model training.

  • ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.

  • EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.

  • RoleArn - The Amazon Resource Name (ARN) that SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that SageMaker can successfully complete model training.

  • StoppingCondition - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long a managed spot training job has to complete.

  • Environment - The environment variables to set in the Docker container.

  • RetryStrategy - The number of times to retry the job when the job fails due to an InternalServerError.

  For more information about SageMaker, see How It Works.

• xAmzTargetSageMakerCreateTransformJob.createTransformJob

  Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

  To perform batch transformations, you create a transform job and use the data that you have readily available.

  In the request body, you provide the following:

  • TransformJobName - Identifies the transform job. The name must be unique within an Amazon Web Services Region in an Amazon Web Services account.

  • ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same Amazon Web Services Region and Amazon Web Services account. For information on creating a model, see CreateModel.

  • TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.

  • TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

  • TransformResources - Identifies the ML compute instances for the transform job.

  For more information about how batch transformation works, see Batch Transform.

• xAmzTargetSageMakerCreateTrial.createTrial

  Creates an SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single SageMaker experiment.

  When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

  You can add tags to a trial and then use the Search API to search for the tags.

  To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.

• xAmzTargetSageMakerCreateTrialComponent.createTrialComponent

  Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.

  Trial components include pre-processing jobs, training jobs, and batch transform jobs.

  When you use SageMaker Studio or the SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the Amazon Web Services SDK for Python (Boto), you must use the logging APIs provided by the SDK.

  You can add tags to a trial component and then use the Search API to search for the tags.

• xAmzTargetSageMakerCreateUserProfile.createUserProfile

  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 IAM Identity Center, a user profile is automatically created. A user profile is the primary holder of settings for an individual user and has a reference to the user's private Amazon Elastic File System (EFS) home directory.

• xAmzTargetSageMakerCreateWorkforce.createWorkforce

  Use this operation to create a workforce. This operation will return an error if a workforce already exists in the Amazon Web Services Region that you specify. You can only create one workforce in each Amazon Web Services Region per Amazon Web Services account.

  If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use the DeleteWorkforce API operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.

  To create a private workforce using Amazon Cognito, you must specify a Cognito user pool in CognitoConfig. You can also create an Amazon Cognito workforce using the Amazon SageMaker console. For more information, see Create a Private Workforce (Amazon Cognito).

  To create a private workforce using your own OIDC Identity Provider (IdP), specify your IdP configuration in OidcConfig. Your OIDC IdP must support groups because groups are used by Ground Truth and Amazon A2I to create work teams. For more information, see Create a Private Workforce (OIDC IdP).

• xAmzTargetSageMakerCreateWorkteam.createWorkteam

  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.

• xAmzTargetSageMakerDeleteAction.deleteAction

  Deletes an action.

• xAmzTargetSageMakerDeleteAlgorithm.deleteAlgorithm

  Removes the specified algorithm from your account.

• xAmzTargetSageMakerDeleteApp.deleteApp

  Used to stop and delete an app.

• xAmzTargetSageMakerDeleteAppImageConfig.deleteAppImageConfig

  Deletes an AppImageConfig.

• xAmzTargetSageMakerDeleteArtifact.deleteArtifact

  Deletes an artifact. Either ArtifactArn or Source must be specified.

• xAmzTargetSageMakerDeleteAssociation.deleteAssociation

  Deletes an association.

• xAmzTargetSageMakerDeleteCodeRepository.deleteCodeRepository

  Deletes the specified Git repository from your account.

• xAmzTargetSageMakerDeleteContext.deleteContext

  Deletes an context.

• xAmzTargetSageMakerDeleteDataQualityJobDefinition.deleteDataQualityJobDefinition

  Deletes a data quality monitoring job definition.

• xAmzTargetSageMakerDeleteDeviceFleet.deleteDeviceFleet

  Deletes a fleet.

• xAmzTargetSageMakerDeleteDomain.deleteDomain

  Used to delete a domain. If you onboarded with IAM mode, you will need to delete your domain to onboard again using IAM Identity Center. Use with caution. All of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.

• xAmzTargetSageMakerDeleteEdgeDeploymentPlan.deleteEdgeDeploymentPlan

  Deletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.

• xAmzTargetSageMakerDeleteEdgeDeploymentStage.deleteEdgeDeploymentStage

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

• xAmzTargetSageMakerDeleteEndpoint.deleteEndpoint

  Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created.

  SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.

  When you delete your endpoint, SageMaker asynchronously deletes associated endpoint resources such as KMS key grants. You might still see these resources in your account for a few minutes after deleting your endpoint. Do not delete or revoke the permissions for your ExecutionRoleArn , otherwise SageMaker cannot delete these resources.

• xAmzTargetSageMakerDeleteEndpointConfig.deleteEndpointConfig

  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.

• xAmzTargetSageMakerDeleteExperiment.deleteExperiment

  Deletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.

• xAmzTargetSageMakerDeleteFeatureGroup.deleteFeatureGroup

  Delete the FeatureGroup and any data that was written to the OnlineStore of the FeatureGroup. Data cannot be accessed from the OnlineStore immediately after DeleteFeatureGroup is called.

  Data written into the OfflineStore will not be deleted. The Amazon Web Services Glue database and tables that are automatically created for your OfflineStore are not deleted.

• xAmzTargetSageMakerDeleteFlowDefinition.deleteFlowDefinition

  Deletes the specified flow definition.

• xAmzTargetSageMakerDeleteHub.deleteHub

  Delete a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerDeleteHubContent.deleteHubContent

  Delete the contents of a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerDeleteHumanTaskUi.deleteHumanTaskUi

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

  To see a list of human task user interfaces (work task templates) in your account, use ListHumanTaskUis. When you delete a worker task template, it no longer appears when you call ListHumanTaskUis.

• xAmzTargetSageMakerDeleteImage.deleteImage

  Deletes a SageMaker image and all versions of the image. The container images aren't deleted.

• xAmzTargetSageMakerDeleteImageVersion.deleteImageVersion

  Deletes a version of a SageMaker image. The container image the version represents isn't deleted.

• xAmzTargetSageMakerDeleteInferenceExperiment.deleteInferenceExperiment

  Deletes an inference experiment.

  This operation does not delete your endpoint, variants, or any underlying resources. This operation only deletes the metadata of your experiment.

• xAmzTargetSageMakerDeleteModel.deleteModel

  Deletes a model. The DeleteModel API deletes only the model entry that was created in SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

• xAmzTargetSageMakerDeleteModelBiasJobDefinition.deleteModelBiasJobDefinition

  Deletes an Amazon SageMaker model bias job definition.

• xAmzTargetSageMakerDeleteModelCard.deleteModelCard

  Deletes an Amazon SageMaker Model Card.

• xAmzTargetSageMakerDeleteModelExplainabilityJobDefinition.deleteModelExplainabilityJobDefinition

  Deletes an Amazon SageMaker model explainability job definition.

• xAmzTargetSageMakerDeleteModelPackage.deleteModelPackage

  Deletes a model package.

  A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

• xAmzTargetSageMakerDeleteModelPackageGroup.deleteModelPackageGroup

  Deletes the specified model group.

• xAmzTargetSageMakerDeleteModelPackageGroupPolicy.deleteModelPackageGroupPolicy

  Deletes a model group resource policy.

• xAmzTargetSageMakerDeleteModelQualityJobDefinition.deleteModelQualityJobDefinition

  Deletes the secified model quality monitoring job definition.

• xAmzTargetSageMakerDeleteMonitoringSchedule.deleteMonitoringSchedule

  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.

• xAmzTargetSageMakerDeleteNotebookInstance.deleteNotebookInstance

  Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.

  When you delete a notebook instance, you lose all of your data. SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

• xAmzTargetSageMakerDeleteNotebookInstanceLifecycleConfig.deleteNotebookInstanceLifecycleConfig

  Deletes a notebook instance lifecycle configuration.

• xAmzTargetSageMakerDeletePipeline.deletePipeline

  Deletes a pipeline if there are no running instances of the pipeline. To delete a pipeline, you must stop all running instances of the pipeline using the StopPipelineExecution API. When you delete a pipeline, all instances of the pipeline are deleted.

• xAmzTargetSageMakerDeleteProject.deleteProject

  Delete the specified project.

• xAmzTargetSageMakerDeleteSpace.deleteSpace

  Used to delete a space.

• xAmzTargetSageMakerDeleteStudioLifecycleConfig.deleteStudioLifecycleConfig

  Deletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You must also remove the Lifecycle Configuration from UserSettings in all Domains and UserProfiles.

• xAmzTargetSageMakerDeleteTags.deleteTags

  Deletes the specified tags from an SageMaker resource.

  To list a resource's tags, use the ListTags API.

  When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

  When you call this API to delete tags from a SageMaker Studio Domain or User Profile, the deleted tags are not removed from Apps that the SageMaker Studio Domain or User Profile launched before you called this API.

• xAmzTargetSageMakerDeleteTrial.deleteTrial

  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.

• xAmzTargetSageMakerDeleteTrialComponent.deleteTrialComponent

  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.

• xAmzTargetSageMakerDeleteUserProfile.deleteUserProfile

  Deletes a user profile. When a user profile is deleted, the user loses access to their EFS volume, including data, notebooks, and other artifacts.

• xAmzTargetSageMakerDeleteWorkforce.deleteWorkforce

  Use this operation to delete a workforce.

  If you want to create a new workforce in an Amazon Web Services Region where a workforce already exists, use this operation to delete the existing workforce and then use CreateWorkforce to create a new workforce.

  If a private workforce contains one or more work teams, you must use the DeleteWorkteam operation to delete all work teams before you delete the workforce. If you try to delete a workforce that contains one or more work teams, you will recieve a ResourceInUse error.

• xAmzTargetSageMakerDeleteWorkteam.deleteWorkteam

  Deletes an existing work team. This operation can't be undone.

• xAmzTargetSageMakerDeregisterDevices.deregisterDevices

  Deregisters the specified devices. After you deregister a device, you will need to re-register the devices.

• xAmzTargetSageMakerDescribeAction.describeAction

  Describes an action.

• xAmzTargetSageMakerDescribeAlgorithm.describeAlgorithm

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

• xAmzTargetSageMakerDescribeApp.describeApp

  Describes the app.

• xAmzTargetSageMakerDescribeAppImageConfig.describeAppImageConfig

  Describes an AppImageConfig.

• xAmzTargetSageMakerDescribeArtifact.describeArtifact

  Describes an artifact.

• xAmzTargetSageMakerDescribeAutoMlJob.describeAutoMlJob

  Returns information about an Amazon SageMaker AutoML job.

• xAmzTargetSageMakerDescribeAutoMlJobV2.describeAutoMlJobV2

  Returns information about an Amazon SageMaker AutoML V2 job.

  This API action is callable through SageMaker Canvas only. Calling it directly from the CLI or an SDK results in an error.

• xAmzTargetSageMakerDescribeCodeRepository.describeCodeRepository

  Gets details about the specified Git repository.

• xAmzTargetSageMakerDescribeCompilationJob.describeCompilationJob

  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.

• xAmzTargetSageMakerDescribeContext.describeContext

  Describes a context.

• xAmzTargetSageMakerDescribeDataQualityJobDefinition.describeDataQualityJobDefinition

  Gets the details of a data quality monitoring job definition.

• xAmzTargetSageMakerDescribeDevice.describeDevice

  Describes the device.

• xAmzTargetSageMakerDescribeDeviceFleet.describeDeviceFleet

  A description of the fleet the device belongs to.

• xAmzTargetSageMakerDescribeDomain.describeDomain

  The description of the domain.

• xAmzTargetSageMakerDescribeEdgeDeploymentPlan.describeEdgeDeploymentPlan

  Describes an edge deployment plan with deployment status per stage.

• xAmzTargetSageMakerDescribeEdgePackagingJob.describeEdgePackagingJob

  A description of edge packaging jobs.

• xAmzTargetSageMakerDescribeEndpoint.describeEndpoint

  Returns the description of an endpoint.

• xAmzTargetSageMakerDescribeEndpointConfig.describeEndpointConfig

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

• xAmzTargetSageMakerDescribeExperiment.describeExperiment

  Provides a list of an experiment's properties.

• xAmzTargetSageMakerDescribeFeatureGroup.describeFeatureGroup

  Use this operation to describe a FeatureGroup. The response includes information on the creation time, FeatureGroup name, the unique identifier for each FeatureGroup, and more.

• xAmzTargetSageMakerDescribeFeatureMetadata.describeFeatureMetadata

  Shows the metadata for a feature within a feature group.

• xAmzTargetSageMakerDescribeFlowDefinition.describeFlowDefinition

  Returns information about the specified flow definition.

• xAmzTargetSageMakerDescribeHub.describeHub

  Describe a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerDescribeHubContent.describeHubContent

  Describe the content of a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerDescribeHumanTaskUi.describeHumanTaskUi

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

• xAmzTargetSageMakerDescribeHyperParameterTuningJob.describeHyperParameterTuningJob

  Gets a description of a hyperparameter tuning job.

• xAmzTargetSageMakerDescribeImage.describeImage

  Describes a SageMaker image.

• xAmzTargetSageMakerDescribeImageVersion.describeImageVersion

  Describes a version of a SageMaker image.

• xAmzTargetSageMakerDescribeInferenceExperiment.describeInferenceExperiment

  Returns details about an inference experiment.

• xAmzTargetSageMakerDescribeInferenceRecommendationsJob.describeInferenceRecommendationsJob

  Provides the results of the Inference Recommender job. One or more recommendation jobs are returned.

• xAmzTargetSageMakerDescribeLabelingJob.describeLabelingJob

  Gets information about a labeling job.

• xAmzTargetSageMakerDescribeLineageGroup.describeLineageGroup

  Provides a list of properties for the requested lineage group. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

• xAmzTargetSageMakerDescribeModel.describeModel

  Describes a model that you created using the CreateModel API.

• xAmzTargetSageMakerDescribeModelBiasJobDefinition.describeModelBiasJobDefinition

  Returns a description of a model bias job definition.

• xAmzTargetSageMakerDescribeModelCard.describeModelCard

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

• xAmzTargetSageMakerDescribeModelCardExportJob.describeModelCardExportJob

  Describes an Amazon SageMaker Model Card export job.

• xAmzTargetSageMakerDescribeModelExplainabilityJobDefinition.describeModelExplainabilityJobDefinition

  Returns a description of a model explainability job definition.

• xAmzTargetSageMakerDescribeModelPackage.describeModelPackage

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

  To create models in SageMaker, buyers can subscribe to model packages listed on Amazon Web Services Marketplace.

• xAmzTargetSageMakerDescribeModelPackageGroup.describeModelPackageGroup

  Gets a description for the specified model group.

• xAmzTargetSageMakerDescribeModelQualityJobDefinition.describeModelQualityJobDefinition

  Returns a description of a model quality job definition.

• xAmzTargetSageMakerDescribeMonitoringSchedule.describeMonitoringSchedule

  Describes the schedule for a monitoring job.

• xAmzTargetSageMakerDescribeNotebookInstance.describeNotebookInstance

  Returns information about a notebook instance.

• xAmzTargetSageMakerDescribeNotebookInstanceLifecycleConfig.describeNotebookInstanceLifecycleConfig

  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.

• xAmzTargetSageMakerDescribePipeline.describePipeline

  Describes the details of a pipeline.

• xAmzTargetSageMakerDescribePipelineDefinitionForExecution.describePipelineDefinitionForExecution

  Describes the details of an execution's pipeline definition.

• xAmzTargetSageMakerDescribePipelineExecution.describePipelineExecution

  Describes the details of a pipeline execution.

• xAmzTargetSageMakerDescribeProcessingJob.describeProcessingJob

  Returns a description of a processing job.

• xAmzTargetSageMakerDescribeProject.describeProject

  Describes the details of a project.

• xAmzTargetSageMakerDescribeSpace.describeSpace

  Describes the space.

• xAmzTargetSageMakerDescribeStudioLifecycleConfig.describeStudioLifecycleConfig

  Describes the Studio Lifecycle Configuration.

• xAmzTargetSageMakerDescribeSubscribedWorkteam.describeSubscribedWorkteam

  Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the Amazon Web Services Marketplace.

• xAmzTargetSageMakerDescribeTrainingJob.describeTrainingJob

  Returns information about a training job.

  Some of the attributes below only appear if the training job successfully starts. If the training job fails, TrainingJobStatus is Failed and, depending on the FailureReason, attributes like TrainingStartTime, TrainingTimeInSeconds, TrainingEndTime, and BillableTimeInSeconds may not be present in the response.

• xAmzTargetSageMakerDescribeTransformJob.describeTransformJob

  Returns information about a transform job.

• xAmzTargetSageMakerDescribeTrial.describeTrial

  Provides a list of a trial's properties.

• xAmzTargetSageMakerDescribeTrialComponent.describeTrialComponent

  Provides a list of a trials component's properties.

• xAmzTargetSageMakerDescribeUserProfile.describeUserProfile

  Describes a user profile. For more information, see CreateUserProfile.

• xAmzTargetSageMakerDescribeWorkforce.describeWorkforce

  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.

• xAmzTargetSageMakerDescribeWorkteam.describeWorkteam

  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).

• xAmzTargetSageMakerDisableSagemakerServicecatalogPortfolio.disableSagemakerServicecatalogPortfolio

  Disables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

• xAmzTargetSageMakerDisassociateTrialComponent.disassociateTrialComponent

  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.

• xAmzTargetSageMakerEnableSagemakerServicecatalogPortfolio.enableSagemakerServicecatalogPortfolio

  Enables using Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

• xAmzTargetSageMakerGetDeviceFleetReport.getDeviceFleetReport

  Describes a fleet.

• xAmzTargetSageMakerGetLineageGroupPolicy.getLineageGroupPolicy

  The resource policy for the lineage group.

• xAmzTargetSageMakerGetModelPackageGroupPolicy.getModelPackageGroupPolicy

  Gets a resource policy that manages access for a model group. For information about resource policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

• xAmzTargetSageMakerGetSagemakerServicecatalogPortfolioStatus.getSagemakerServicecatalogPortfolioStatus

  Gets the status of Service Catalog in SageMaker. Service Catalog is used to create SageMaker projects.

• xAmzTargetSageMakerGetSearchSuggestions.getSearchSuggestions

  An auto-complete API for the search functionality in the SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

• xAmzTargetSageMakerImportHubContent.importHubContent

  Import hub content.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerListActions.listActions

  Lists the actions in your account and their properties.

• xAmzTargetSageMakerListAlgorithms.listAlgorithms

  Lists the machine learning algorithms that have been created.

• xAmzTargetSageMakerListAliases.listAliases

  Lists the aliases of a specified image or image version.

• xAmzTargetSageMakerListAppImageConfigs.listAppImageConfigs

  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.

• xAmzTargetSageMakerListApps.listApps

  Lists apps.

• xAmzTargetSageMakerListArtifacts.listArtifacts

  Lists the artifacts in your account and their properties.

• xAmzTargetSageMakerListAssociations.listAssociations

  Lists the associations in your account and their properties.

• xAmzTargetSageMakerListAutoMlJobs.listAutoMlJobs

  Request a list of jobs.

• xAmzTargetSageMakerListCandidatesForAutoMlJob.listCandidatesForAutoMlJob

  List the candidates created for the job.

• xAmzTargetSageMakerListCodeRepositories.listCodeRepositories

  Gets a list of the Git repositories in your account.

• xAmzTargetSageMakerListCompilationJobs.listCompilationJobs

  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.

• xAmzTargetSageMakerListContexts.listContexts

  Lists the contexts in your account and their properties.

• xAmzTargetSageMakerListDataQualityJobDefinitions.listDataQualityJobDefinitions

  Lists the data quality job definitions in your account.

• xAmzTargetSageMakerListDeviceFleets.listDeviceFleets

  Returns a list of devices in the fleet.

• xAmzTargetSageMakerListDevices.listDevices

  A list of devices.

• xAmzTargetSageMakerListDomains.listDomains

  Lists the domains.

• xAmzTargetSageMakerListEdgeDeploymentPlans.listEdgeDeploymentPlans

  Lists all edge deployment plans.

• xAmzTargetSageMakerListEdgePackagingJobs.listEdgePackagingJobs

  Returns a list of edge packaging jobs.

• xAmzTargetSageMakerListEndpointConfigs.listEndpointConfigs

  Lists endpoint configurations.

• xAmzTargetSageMakerListEndpoints.listEndpoints

  Lists endpoints.

• xAmzTargetSageMakerListExperiments.listExperiments

  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.

• xAmzTargetSageMakerListFeatureGroups.listFeatureGroups

  List FeatureGroups based on given filter and order.

• xAmzTargetSageMakerListFlowDefinitions.listFlowDefinitions

  Returns information about the flow definitions in your account.

• xAmzTargetSageMakerListHubContents.listHubContents

  List the contents of a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerListHubContentVersions.listHubContentVersions

  List hub content versions.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerListHubs.listHubs

  List all existing hubs.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerListHumanTaskUis.listHumanTaskUis

  Returns information about the human task user interfaces in your account.

• xAmzTargetSageMakerListHyperParameterTuningJobs.listHyperParameterTuningJobs

  Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

• xAmzTargetSageMakerListImages.listImages

  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.

• xAmzTargetSageMakerListImageVersions.listImageVersions

  Lists the versions of a specified image and their properties. The list can be filtered by creation time or modified time.

• xAmzTargetSageMakerListInferenceExperiments.listInferenceExperiments

  Returns the list of all inference experiments.

• xAmzTargetSageMakerListInferenceRecommendationsJobs.listInferenceRecommendationsJobs

  Lists recommendation jobs that satisfy various filters.

• xAmzTargetSageMakerListInferenceRecommendationsJobSteps.listInferenceRecommendationsJobSteps

  Returns a list of the subtasks for an Inference Recommender job.

  The supported subtasks are benchmarks, which evaluate the performance of your model on different instance types.

• xAmzTargetSageMakerListLabelingJobs.listLabelingJobs

  Gets a list of labeling jobs.

• xAmzTargetSageMakerListLabelingJobsForWorkteam.listLabelingJobsForWorkteam

  Gets a list of labeling jobs assigned to a specified work team.

• xAmzTargetSageMakerListLineageGroups.listLineageGroups

  A list of lineage groups shared with your Amazon Web Services account. For more information, see Cross-Account Lineage Tracking in the Amazon SageMaker Developer Guide.

• xAmzTargetSageMakerListModelBiasJobDefinitions.listModelBiasJobDefinitions

  Lists model bias jobs definitions that satisfy various filters.

• xAmzTargetSageMakerListModelCardExportJobs.listModelCardExportJobs

  List the export jobs for the Amazon SageMaker Model Card.

• xAmzTargetSageMakerListModelCards.listModelCards

  List existing model cards.

• xAmzTargetSageMakerListModelCardVersions.listModelCardVersions

  List existing versions of an Amazon SageMaker Model Card.

• xAmzTargetSageMakerListModelExplainabilityJobDefinitions.listModelExplainabilityJobDefinitions

  Lists model explainability job definitions that satisfy various filters.

• xAmzTargetSageMakerListModelMetadata.listModelMetadata

  Lists the domain, framework, task, and model name of standard machine learning models found in common model zoos.

• xAmzTargetSageMakerListModelPackageGroups.listModelPackageGroups

  Gets a list of the model groups in your Amazon Web Services account.

• xAmzTargetSageMakerListModelPackages.listModelPackages

  Lists the model packages that have been created.

• xAmzTargetSageMakerListModelQualityJobDefinitions.listModelQualityJobDefinitions

  Gets a list of model quality monitoring job definitions in your account.

• xAmzTargetSageMakerListModels.listModels

  Lists models created with the CreateModel API.

• xAmzTargetSageMakerListMonitoringAlertHistory.listMonitoringAlertHistory

  Gets a list of past alerts in a model monitoring schedule.

• xAmzTargetSageMakerListMonitoringAlerts.listMonitoringAlerts

  Gets the alerts for a single monitoring schedule.

• xAmzTargetSageMakerListMonitoringExecutions.listMonitoringExecutions

  Returns list of all monitoring job executions.

• xAmzTargetSageMakerListMonitoringSchedules.listMonitoringSchedules

  Returns list of all monitoring schedules.

• xAmzTargetSageMakerListNotebookInstanceLifecycleConfigs.listNotebookInstanceLifecycleConfigs

  Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

• xAmzTargetSageMakerListNotebookInstances.listNotebookInstances

  Returns a list of the SageMaker notebook instances in the requester's account in an Amazon Web Services Region.

• xAmzTargetSageMakerListPipelineExecutions.listPipelineExecutions

  Gets a list of the pipeline executions.

• xAmzTargetSageMakerListPipelineExecutionSteps.listPipelineExecutionSteps

  Gets a list of PipeLineExecutionStep objects.

• xAmzTargetSageMakerListPipelineParametersForExecution.listPipelineParametersForExecution

  Gets a list of parameters for a pipeline execution.

• xAmzTargetSageMakerListPipelines.listPipelines

  Gets a list of pipelines.

• xAmzTargetSageMakerListProcessingJobs.listProcessingJobs

  Lists processing jobs that satisfy various filters.

• xAmzTargetSageMakerListProjects.listProjects

  Gets a list of the projects in an Amazon Web Services account.

• xAmzTargetSageMakerListSpaces.listSpaces

  Lists spaces.

• xAmzTargetSageMakerListStageDevices.listStageDevices

  Lists devices allocated to the stage, containing detailed device information and deployment status.

• xAmzTargetSageMakerListStudioLifecycleConfigs.listStudioLifecycleConfigs

  Lists the Studio Lifecycle Configurations in your Amazon Web Services Account.

• xAmzTargetSageMakerListSubscribedWorkteams.listSubscribedWorkteams

  Gets a list of the work teams that you are subscribed to in the Amazon Web Services Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

• xAmzTargetSageMakerListTags.listTags

  Returns the tags for the specified SageMaker resource.

• xAmzTargetSageMakerListTrainingJobs.listTrainingJobs

  Lists training jobs.

  When StatusEquals and MaxResults are set at the same time, the MaxResults number of training jobs are first retrieved ignoring the StatusEquals parameter and then they are filtered by the StatusEquals parameter, which is returned as a response.

  For example, if ListTrainingJobs is invoked with the following parameters:

  { ... MaxResults: 100, StatusEquals: InProgress ... }

  First, 100 trainings jobs with any status, including those other than InProgress, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of InProgress are returned.

  You can quickly test the API using the following Amazon Web Services CLI code.

  aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress

• xAmzTargetSageMakerListTrainingJobsForHyperParameterTuningJob.listTrainingJobsForHyperParameterTuningJob

  Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

• xAmzTargetSageMakerListTransformJobs.listTransformJobs

  Lists transform jobs.

• xAmzTargetSageMakerListTrialComponents.listTrialComponents

  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

• xAmzTargetSageMakerListTrials.listTrials

  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.

• xAmzTargetSageMakerListUserProfiles.listUserProfiles

  Lists user profiles.

• xAmzTargetSageMakerListWorkforces.listWorkforces

  Use this operation to list all private and vendor workforces in an Amazon Web Services Region. Note that you can only have one private workforce per Amazon Web Services Region.

• xAmzTargetSageMakerListWorkteams.listWorkteams

  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.

• xAmzTargetSageMakerPutModelPackageGroupPolicy.putModelPackageGroupPolicy

  Adds a resouce policy to control access to a model group. For information about resoure policies, see Identity-based policies and resource-based policies in the Amazon Web Services Identity and Access Management User Guide..

• xAmzTargetSageMakerQueryLineage.queryLineage

  Use this action to inspect your lineage and discover relationships between entities. For more information, see Querying Lineage Entities in the Amazon SageMaker Developer Guide.

• xAmzTargetSageMakerRegisterDevices.registerDevices

  Register devices.

• xAmzTargetSageMakerRenderUiTemplate.renderUiTemplate

  Renders the UI template so that you can preview the worker's experience.

• xAmzTargetSageMakerRetryPipelineExecution.retryPipelineExecution

  Retry the execution of the pipeline.

• xAmzTargetSageMakerSearch.search

  Finds SageMaker resources that match a search query. Matching resources are returned as a list of SearchRecord objects in the response. You can sort the search results by any resource property in a ascending or descending order.

  You can query against the following value types: numeric, text, Boolean, and timestamp.

  The Search API may provide access to otherwise restricted data. See Amazon SageMaker API Permissions: Actions, Permissions, and Resources Reference for more information.

• xAmzTargetSageMakerSendPipelineExecutionStepFailure.sendPipelineExecutionStepFailure

  Notifies the pipeline that the execution of a callback step failed, along with a message describing why. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

• xAmzTargetSageMakerSendPipelineExecutionStepSuccess.sendPipelineExecutionStepSuccess

  Notifies the pipeline that the execution of a callback step succeeded and provides a list of the step's output parameters. When a callback step is run, the pipeline generates a callback token and includes the token in a message sent to Amazon Simple Queue Service (Amazon SQS).

• xAmzTargetSageMakerStartEdgeDeploymentStage.startEdgeDeploymentStage

  Starts a stage in an edge deployment plan.

• xAmzTargetSageMakerStartInferenceExperiment.startInferenceExperiment

  Starts an inference experiment.

• xAmzTargetSageMakerStartMonitoringSchedule.startMonitoringSchedule

  Starts a previously stopped monitoring schedule.

  By default, when you successfully create a new schedule, the status of a monitoring schedule is scheduled.

• xAmzTargetSageMakerStartNotebookInstance.startNotebookInstance

  Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

• xAmzTargetSageMakerStartPipelineExecution.startPipelineExecution

  Starts a pipeline execution.

• xAmzTargetSageMakerStopAutoMlJob.stopAutoMlJob

  A method for forcing a running job to shut down.

• xAmzTargetSageMakerStopCompilationJob.stopCompilationJob

  Stops a model compilation job.

  To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.

  When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobStatus to Stopped.

• xAmzTargetSageMakerStopEdgeDeploymentStage.stopEdgeDeploymentStage

  Stops a stage in an edge deployment plan.

• xAmzTargetSageMakerStopEdgePackagingJob.stopEdgePackagingJob

  Request to stop an edge packaging job.

• xAmzTargetSageMakerStopHyperParameterTuningJob.stopHyperParameterTuningJob

  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.

• xAmzTargetSageMakerStopInferenceExperiment.stopInferenceExperiment

  Stops an inference experiment.

• xAmzTargetSageMakerStopInferenceRecommendationsJob.stopInferenceRecommendationsJob

  Stops an Inference Recommender job.

• xAmzTargetSageMakerStopLabelingJob.stopLabelingJob

  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.

• xAmzTargetSageMakerStopMonitoringSchedule.stopMonitoringSchedule

  Stops a previously started monitoring schedule.

• xAmzTargetSageMakerStopNotebookInstance.stopNotebookInstance

  Terminates the ML compute instance. Before terminating the instance, SageMaker disconnects the ML storage volume from it. SageMaker preserves the ML storage volume. SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.

  To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

• xAmzTargetSageMakerStopPipelineExecution.stopPipelineExecution

  Stops a pipeline execution.

  Callback Step

  A pipeline execution won't stop while a callback step is running. When you call StopPipelineExecution on a pipeline execution with a running callback step, SageMaker Pipelines sends an additional Amazon SQS message to the specified SQS queue. The body of the SQS message contains a "Status" field which is set to "Stopping".

  You should add logic to your Amazon SQS message consumer to take any needed action (for example, resource cleanup) upon receipt of the message followed by a call to SendPipelineExecutionStepSuccess or SendPipelineExecutionStepFailure.

  Only when SageMaker Pipelines receives one of these calls will it stop the pipeline execution.

  Lambda Step

  A pipeline execution can't be stopped while a lambda step is running because the Lambda function invoked by the lambda step can't be stopped. If you attempt to stop the execution while the Lambda function is running, the pipeline waits for the Lambda function to finish or until the timeout is hit, whichever occurs first, and then stops. If the Lambda function finishes, the pipeline execution status is Stopped. If the timeout is hit the pipeline execution status is Failed.

• xAmzTargetSageMakerStopProcessingJob.stopProcessingJob

  Stops a processing job.

• xAmzTargetSageMakerStopTrainingJob.stopTrainingJob

  Stops a training job. To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

  When it receives a StopTrainingJob request, SageMaker changes the status of the job to Stopping. After SageMaker stops the job, it sets the status to Stopped.

• xAmzTargetSageMakerStopTransformJob.stopTransformJob

  Stops a batch transform job.

  When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a batch transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

• xAmzTargetSageMakerUpdateAction.updateAction

  Updates an action.

• xAmzTargetSageMakerUpdateAppImageConfig.updateAppImageConfig

  Updates the properties of an AppImageConfig.

• xAmzTargetSageMakerUpdateArtifact.updateArtifact

  Updates an artifact.

• xAmzTargetSageMakerUpdateCodeRepository.updateCodeRepository

  Updates the specified Git repository with the specified values.

• xAmzTargetSageMakerUpdateContext.updateContext

  Updates a context.

• xAmzTargetSageMakerUpdateDeviceFleet.updateDeviceFleet

  Updates a fleet of devices.

• xAmzTargetSageMakerUpdateDevices.updateDevices

  Updates one or more devices in a fleet.

• xAmzTargetSageMakerUpdateDomain.updateDomain

  Updates the default settings for new user profiles in the domain.

• xAmzTargetSageMakerUpdateEndpoint.updateEndpoint

  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 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.

• xAmzTargetSageMakerUpdateEndpointWeightsAndCapacities.updateEndpointWeightsAndCapacities

  Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

• xAmzTargetSageMakerUpdateExperiment.updateExperiment

  Adds, updates, or removes the description of an experiment. Updates the display name of an experiment.

• xAmzTargetSageMakerUpdateFeatureGroup.updateFeatureGroup

  Updates the feature group.

• xAmzTargetSageMakerUpdateFeatureMetadata.updateFeatureMetadata

  Updates the description and parameters of the feature group.

• xAmzTargetSageMakerUpdateHub.updateHub

  Update a hub.

  Hub APIs are only callable through SageMaker Studio.

• xAmzTargetSageMakerUpdateImage.updateImage

  Updates the properties of a SageMaker image. To change the image's tags, use the AddTags and DeleteTags APIs.

• xAmzTargetSageMakerUpdateImageVersion.updateImageVersion

  Updates the properties of a SageMaker image version.

• xAmzTargetSageMakerUpdateInferenceExperiment.updateInferenceExperiment

  Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.

• xAmzTargetSageMakerUpdateModelCard.updateModelCard

  Update an Amazon SageMaker Model Card.

  You cannot update both model card content and model card status in a single call.

• xAmzTargetSageMakerUpdateModelPackage.updateModelPackage

  Updates a versioned model.

• xAmzTargetSageMakerUpdateMonitoringAlert.updateMonitoringAlert

  Update the parameters of a model monitor alert.

• xAmzTargetSageMakerUpdateMonitoringSchedule.updateMonitoringSchedule

  Updates a previously created schedule.

• xAmzTargetSageMakerUpdateNotebookInstance.updateNotebookInstance

  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.

• xAmzTargetSageMakerUpdateNotebookInstanceLifecycleConfig.updateNotebookInstanceLifecycleConfig

  Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

• xAmzTargetSageMakerUpdatePipeline.updatePipeline

  Updates a pipeline.

• xAmzTargetSageMakerUpdatePipelineExecution.updatePipelineExecution

  Updates a pipeline execution.

• xAmzTargetSageMakerUpdateProject.updateProject

  Updates a machine learning (ML) project that is created from a template that sets up an ML pipeline from training to deploying an approved model.

  You must not update a project that is in use. If you update the ServiceCatalogProvisioningUpdateDetails of a project that is active or being created, or updated, you may lose resources already created by the project.

• xAmzTargetSageMakerUpdateSpace.updateSpace

  Updates the settings of a space.

• xAmzTargetSageMakerUpdateTrainingJob.updateTrainingJob

  Update a model training job to request a new Debugger profiling configuration or to change warm pool retention length.

• xAmzTargetSageMakerUpdateTrial.updateTrial

  Updates the display name of a trial.

• xAmzTargetSageMakerUpdateTrialComponent.updateTrialComponent

  Updates one or more properties of a trial component.

• xAmzTargetSageMakerUpdateUserProfile.updateUserProfile

  Updates a user profile.

• xAmzTargetSageMakerUpdateWorkforce.updateWorkforce

  Use this operation to update your workforce. You can use this operation to require that workers use specific IP addresses to work on tasks and to update your OpenID Connect (OIDC) Identity Provider (IdP) workforce configuration.

  The worker portal is now supported in VPC and public internet.

  Use SourceIpConfig to restrict worker access to tasks to a specific range of IP addresses. You specify allowed IP addresses by creating a list of up to ten CIDRs. By default, a workforce isn't restricted to specific IP addresses. If you specify a range of IP addresses, workers who attempt to access tasks using any IP address outside the specified range are denied and get a Not Found error message on the worker portal.

  To restrict access to all the workers in public internet, add the SourceIpConfig CIDR value as "10.0.0.0/16".

  Amazon SageMaker does not support Source Ip restriction for worker portals in VPC.

  Use OidcConfig to update the configuration of a workforce created using your own OIDC IdP.

  You can only update your OIDC IdP configuration when there are no work teams associated with your workforce. You can delete work teams using the DeleteWorkteam operation.

  After restricting access to a range of IP addresses or updating your OIDC IdP configuration with this operation, you can view details about your update workforce using the DescribeWorkforce operation.

  This operation only applies to private workforces.

• xAmzTargetSageMakerUpdateWorkteam.updateWorkteam

  Updates an existing work team with new member definitions or description.

• openapi.previewSpec

  Preview an OpenAPI document before adding it as a source

• openapi.addSource

  Add an OpenAPI source and register its operations as tools