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Amazon SageMaker Service API

by amazonaws.com

API keyHTTPSCloud Platforms
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Overview

<p>Provides APIs for creating and managing SageMaker resources. </p> <p>Other Resources:</p> <ul> <li> <p> <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/whatis.html#first-time-user">SageMaker Developer Guide</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/augmented-ai/2019-11-07/APIReference/Welcome.html">Amazon Augmented AI Runtime API Reference</a> </p> </li> </ul>

Amazon SageMaker Service is a cloud platforms API from amazonaws.com. Requests are authenticated with an API key. It is served over HTTPS. Its OpenAPI description documents 302 endpoints.

At a glance

Authentication
API key
HTTPS
Yes
CORS
Unknown
Category
Cloud Platforms
Version
2017-07-24
Endpoints
302
OpenAPI
Specification ↗
Spec updated
2020-05-09

Endpoints

MethodPathWhat it does
POST/#X-Amz-Target=SageMaker.AddAssociationCreates an <i>association</i> between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated
POST/#X-Amz-Target=SageMaker.AddTags<p>Adds or overwrites one or more tags for the specified SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs,
POST/#X-Amz-Target=SageMaker.AssociateTrialComponentAssociates 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 <a
POST/#X-Amz-Target=SageMaker.BatchDescribeModelPackageThis action batch describes a list of versioned model packages
POST/#X-Amz-Target=SageMaker.CreateActionCreates an <i>action</i>. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally
POST/#X-Amz-Target=SageMaker.CreateAlgorithmCreate a machine learning algorithm that you can use in SageMaker and list in the Amazon Web Services Marketplace.
POST/#X-Amz-Target=SageMaker.CreateAppCreates a running app for the specified UserProfile. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Domain, an
POST/#X-Amz-Target=SageMaker.CreateAppImageConfigCreates a configuration for running a SageMaker image as a KernelGateway app. The configuration specifies the Amazon Elastic File System (EFS) storage volume on
POST/#X-Amz-Target=SageMaker.CreateArtifactCreates an <i>artifact</i>. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset
POST/#X-Amz-Target=SageMaker.CreateAutoMLJob<p>Creates an Autopilot job.</p> <p>Find the best-performing model after you run an Autopilot job by calling <a href="https://docs.aws.amazon.com/sagemaker/late
POST/#X-Amz-Target=SageMaker.CreateAutoMLJobV2<p>Creates an Amazon SageMaker AutoML job that uses non-tabular data such as images or text for Computer Vision or Natural Language Processing problems.</p> <p>
POST/#X-Amz-Target=SageMaker.CreateCodeRepository<p>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 con
POST/#X-Amz-Target=SageMaker.CreateCompilationJob<p>Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (
POST/#X-Amz-Target=SageMaker.CreateContextCreates a <i>context</i>. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are
POST/#X-Amz-Target=SageMaker.CreateDataQualityJobDefinitionCreates a definition for a job that monitors data quality and drift. For information about model monitor, see <a href="https://docs.aws.amazon.com/sagemaker/lat
POST/#X-Amz-Target=SageMaker.CreateDeviceFleetCreates a device fleet.
POST/#X-Amz-Target=SageMaker.CreateDomain<p>Creates a <code>Domain</code> used by Amazon SageMaker Studio. A domain consists of an associated Amazon Elastic File System (EFS) volume, a list of authoriz
POST/#X-Amz-Target=SageMaker.CreateEdgeDeploymentPlanCreates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.
POST/#X-Amz-Target=SageMaker.CreateEdgeDeploymentStageCreates a new stage in an existing edge deployment plan.
POST/#X-Amz-Target=SageMaker.CreateEdgePackagingJobStarts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. A
POST/#X-Amz-Target=SageMaker.CreateEndpoint<p>Creates an endpoint using the endpoint configuration specified in the request. SageMaker uses the endpoint to provision resources and deploy models. You crea
POST/#X-Amz-Target=SageMaker.CreateEndpointConfig<p>Creates an endpoint configuration that SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using
POST/#X-Amz-Target=SageMaker.CreateExperiment<p>Creates a SageMaker <i>experiment</i>. An experiment is a collection of <i>trials</i> that are observed, compared and evaluated as a group. A trial is a set
POST/#X-Amz-Target=SageMaker.CreateFeatureGroup<p>Create a new <code>FeatureGroup</code>. A <code>FeatureGroup</code> is a group of <code>Features</code> defined in the <code>FeatureStore</code> to describe
POST/#X-Amz-Target=SageMaker.CreateFlowDefinitionCreates a flow definition.
POST/#X-Amz-Target=SageMaker.CreateHub<p>Create a hub.</p> <note> <p>Hub APIs are only callable through SageMaker Studio.</p> </note>
POST/#X-Amz-Target=SageMaker.CreateHumanTaskUiDefines 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
POST/#X-Amz-Target=SageMaker.CreateHyperParameterTuningJob<p>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 alg
POST/#X-Amz-Target=SageMaker.CreateImageCreates a custom SageMaker image. A SageMaker image is a set of image versions. Each image version represents a container image stored in Amazon Elastic Contain
POST/#X-Amz-Target=SageMaker.CreateImageVersionCreates a version of the SageMaker image specified by <code>ImageName</code>. The version represents the Amazon Elastic Container Registry (ECR) container image
POST/#X-Amz-Target=SageMaker.CreateInferenceExperiment<p> Creates an inference experiment using the configurations specified in the request. </p> <p> Use this API to setup and schedule an experiment to compare mode
POST/#X-Amz-Target=SageMaker.CreateInferenceRecommendationsJobStarts a recommendation job. You can create either an instance recommendation or load test job.
POST/#X-Amz-Target=SageMaker.CreateLabelingJob<p>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. </p> <p>You c
POST/#X-Amz-Target=SageMaker.CreateModel<p>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 th
POST/#X-Amz-Target=SageMaker.CreateModelBiasJobDefinitionCreates the definition for a model bias job.
POST/#X-Amz-Target=SageMaker.CreateModelCard<p>Creates an Amazon SageMaker Model Card.</p> <p>For information about how to use model cards, see <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/mod
POST/#X-Amz-Target=SageMaker.CreateModelCardExportJobCreates an Amazon SageMaker Model Card export job.
POST/#X-Amz-Target=SageMaker.CreateModelExplainabilityJobDefinitionCreates the definition for a model explainability job.
POST/#X-Amz-Target=SageMaker.CreateModelPackage<p>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
POST/#X-Amz-Target=SageMaker.CreateModelPackageGroupCreates a model group. A model group contains a group of model versions.
POST/#X-Amz-Target=SageMaker.CreateModelQualityJobDefinitionCreates a definition for a job that monitors model quality and drift. For information about model monitor, see <a href="https://docs.aws.amazon.com/sagemaker/la
POST/#X-Amz-Target=SageMaker.CreateMonitoringScheduleCreates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
POST/#X-Amz-Target=SageMaker.CreateNotebookInstance<p>Creates an SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook. </p> <p>In a <code>Cre
POST/#X-Amz-Target=SageMaker.CreateNotebookInstanceLifecycleConfig<p>Creates a lifecycle configuration that you can associate with a notebook instance. A <i>lifecycle configuration</i> is a collection of shell scripts that run
POST/#X-Amz-Target=SageMaker.CreatePipelineCreates a pipeline using a JSON pipeline definition.
POST/#X-Amz-Target=SageMaker.CreatePresignedDomainUrl<p>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, a
POST/#X-Amz-Target=SageMaker.CreatePresignedNotebookInstanceUrl<p>Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the SageMaker console, when you choose <code>Open</code> next to
POST/#X-Amz-Target=SageMaker.CreateProcessingJobCreates a processing job.
POST/#X-Amz-Target=SageMaker.CreateProjectCreates 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.
POST/#X-Amz-Target=SageMaker.CreateSpaceCreates a space used for real time collaboration in a Domain.
POST/#X-Amz-Target=SageMaker.CreateStudioLifecycleConfigCreates a new Studio Lifecycle Configuration.
POST/#X-Amz-Target=SageMaker.CreateTrainingJob<p>Starts a model training job. After training completes, SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify. </p> <p>If yo
POST/#X-Amz-Target=SageMaker.CreateTransformJob<p>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
POST/#X-Amz-Target=SageMaker.CreateTrial<p>Creates an SageMaker <i>trial</i>. A trial is a set of steps called <i>trial components</i> that produce a machine learning model. A trial is part of a singl
POST/#X-Amz-Target=SageMaker.CreateTrialComponent<p>Creates a <i>trial component</i>, which is a stage of a machine learning <i>trial</i>. A trial is composed of one or more trial components. A trial component
POST/#X-Amz-Target=SageMaker.CreateUserProfileCreates 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, report
POST/#X-Amz-Target=SageMaker.CreateWorkforce<p>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 specif
POST/#X-Amz-Target=SageMaker.CreateWorkteam<p>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
POST/#X-Amz-Target=SageMaker.DeleteActionDeletes an action.
POST/#X-Amz-Target=SageMaker.DeleteAlgorithmRemoves the specified algorithm from your account.
POST/#X-Amz-Target=SageMaker.DeleteAppUsed to stop and delete an app.
POST/#X-Amz-Target=SageMaker.DeleteAppImageConfigDeletes an AppImageConfig.
POST/#X-Amz-Target=SageMaker.DeleteArtifactDeletes an artifact. Either <code>ArtifactArn</code> or <code>Source</code> must be specified.
POST/#X-Amz-Target=SageMaker.DeleteAssociationDeletes an association.
POST/#X-Amz-Target=SageMaker.DeleteCodeRepositoryDeletes the specified Git repository from your account.
POST/#X-Amz-Target=SageMaker.DeleteContextDeletes an context.
POST/#X-Amz-Target=SageMaker.DeleteDataQualityJobDefinitionDeletes a data quality monitoring job definition.
POST/#X-Amz-Target=SageMaker.DeleteDeviceFleetDeletes a fleet.
POST/#X-Amz-Target=SageMaker.DeleteDomainUsed 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 o
POST/#X-Amz-Target=SageMaker.DeleteEdgeDeploymentPlanDeletes an edge deployment plan if (and only if) all the stages in the plan are inactive or there are no stages in the plan.
POST/#X-Amz-Target=SageMaker.DeleteEdgeDeploymentStageDelete a stage in an edge deployment plan if (and only if) the stage is inactive.
POST/#X-Amz-Target=SageMaker.DeleteEndpoint<p>Deletes an endpoint. SageMaker frees up all of the resources that were deployed when the endpoint was created. </p> <p>SageMaker retires any custom KMS key g
POST/#X-Amz-Target=SageMaker.DeleteEndpointConfig<p>Deletes an endpoint configuration. The <code>DeleteEndpointConfig</code> API deletes only the specified configuration. It does not delete endpoints created u
POST/#X-Amz-Target=SageMaker.DeleteExperimentDeletes an SageMaker experiment. All trials associated with the experiment must be deleted first. Use the <a href="https://docs.aws.amazon.com/sagemaker/latest/
POST/#X-Amz-Target=SageMaker.DeleteFeatureGroup<p>Delete the <code>FeatureGroup</code> and any data that was written to the <code>OnlineStore</code> of the <code>FeatureGroup</code>. Data cannot be accessed
POST/#X-Amz-Target=SageMaker.DeleteFlowDefinitionDeletes the specified flow definition.
POST/#X-Amz-Target=SageMaker.DeleteHub<p>Delete a hub.</p> <note> <p>Hub APIs are only callable through SageMaker Studio.</p> </note>
POST/#X-Amz-Target=SageMaker.DeleteHubContent<p>Delete the contents of a hub.</p> <note> <p>Hub APIs are only callable through SageMaker Studio.</p> </note>
POST/#X-Amz-Target=SageMaker.DeleteHumanTaskUi<p>Use this operation to delete a human task user interface (worker task template).</p> <p> To see a list of human task user interfaces (work task templates) in
POST/#X-Amz-Target=SageMaker.DeleteImageDeletes a SageMaker image and all versions of the image. The container images aren't deleted.
POST/#X-Amz-Target=SageMaker.DeleteImageVersionDeletes a version of a SageMaker image. The container image the version represents isn't deleted.
POST/#X-Amz-Target=SageMaker.DeleteInferenceExperiment<p>Deletes an inference experiment.</p> <note> <p> This operation does not delete your endpoint, variants, or any underlying resources. This operation only dele
POST/#X-Amz-Target=SageMaker.DeleteModelDeletes a model. The <code>DeleteModel</code> API deletes only the model entry that was created in SageMaker when you called the <code>CreateModel</code> API. I
POST/#X-Amz-Target=SageMaker.DeleteModelBiasJobDefinitionDeletes an Amazon SageMaker model bias job definition.
POST/#X-Amz-Target=SageMaker.DeleteModelCardDeletes an Amazon SageMaker Model Card.
POST/#X-Amz-Target=SageMaker.DeleteModelExplainabilityJobDefinitionDeletes an Amazon SageMaker model explainability job definition.
POST/#X-Amz-Target=SageMaker.DeleteModelPackage<p>Deletes a model package.</p> <p>A model package is used to create SageMaker models or list on Amazon Web Services Marketplace. Buyers can subscribe to model
POST/#X-Amz-Target=SageMaker.DeleteModelPackageGroupDeletes the specified model group.
POST/#X-Amz-Target=SageMaker.DeleteModelPackageGroupPolicyDeletes a model group resource policy.
POST/#X-Amz-Target=SageMaker.DeleteModelQualityJobDefinitionDeletes the secified model quality monitoring job definition.
POST/#X-Amz-Target=SageMaker.DeleteMonitoringScheduleDeletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.
POST/#X-Amz-Target=SageMaker.DeleteNotebookInstance<p> Deletes an SageMaker notebook instance. Before you can delete a notebook instance, you must call the <code>StopNotebookInstance</code> API. </p> <important>
POST/#X-Amz-Target=SageMaker.DeleteNotebookInstanceLifecycleConfigDeletes a notebook instance lifecycle configuration.
POST/#X-Amz-Target=SageMaker.DeletePipelineDeletes 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 <code>
POST/#X-Amz-Target=SageMaker.DeleteProjectDelete the specified project.
POST/#X-Amz-Target=SageMaker.DeleteSpaceUsed to delete a space.
POST/#X-Amz-Target=SageMaker.DeleteStudioLifecycleConfigDeletes the Studio Lifecycle Configuration. In order to delete the Lifecycle Configuration, there must be no running apps using the Lifecycle Configuration. You
POST/#X-Amz-Target=SageMaker.DeleteTags<p>Deletes the specified tags from an SageMaker resource.</p> <p>To list a resource's tags, use the <code>ListTags</code> API. </p> <note> <p>When you call this
POST/#X-Amz-Target=SageMaker.DeleteTrialDeletes the specified trial. All trial components that make up the trial must be deleted first. Use the <a href="https://docs.aws.amazon.com/sagemaker/latest/AP
POST/#X-Amz-Target=SageMaker.DeleteTrialComponentDeletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a tria

Showing the first 100 of 302 endpoints; the full list is in the documentation.

Questions

Does the Amazon SageMaker Service API need an API key?

Yes. The Amazon SageMaker Service API uses API key authentication; the documentation explains how to get credentials.

Does the Amazon SageMaker Service API support HTTPS?

Yes, the Amazon SageMaker Service API is served over HTTPS.

How many endpoints does the Amazon SageMaker Service API have?

Its OpenAPI description lists 302 endpoints in version 2017-07-24. The first 100 are shown on this page.

What are alternatives to the Amazon SageMaker Service API?

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