444 actions in the sagemaker service. Select an action to see its access level, resource ARNs, condition keys, and a copy-paste IAM policy.
Grants permission to access model package that can be used in Amazon SageMaker training or hosting services
Grants permission to associate a lineage entity (artifact, context, action, experiment, experiment-trial-component) to each other
Grants permission to add or overwrite one or more tags for the specified Amazon SageMaker resource
Grants permission to associate a trial component with a trial
Grants permission to attach an Amazon EBS volume to a SageMaker HyperPod cluster node
Grants permission to add multiple nodes at a time to a SageMaker HyperPod cluster
Grants permission to batch delete SageMaker HyperPod cluster nodes
Grants permission to describe one or more ModelPackages
Grants permission to retrieve metrics associated with SageMaker Resources such as Training Jobs or Trial Components
Grants permission to get a batch of records from one or more feature groups
Grants permission to publish metrics associated with a SageMaker Resource such as a Training Job or Trial Component
Grants permission to put a batch of records to one or more feature groups
Grants permission to invoke MLflow APIs
Grants permission for Partner App SDK to access the Partner App for reading or writing data use cases
Grants permission to use bearer token in SageMaker Job and Inference runtime endpoints APIs
Grants permission to mark a rollout as complete for a job
Grants permission to create an action
Grants permission to create an AI benchmark job
Grants permission to create an AI recommendation job
Grants permission to create an AI workload configuration
Grants permission to create an algorithm
Grants permission to create an App for a SageMaker UserProfile or Space
Grants permission to create an AppImageConfig
Grants permission to create an artifact
Grants permission to create an AutoML job
Grants permission to create a V2 AutoML job
Grants permission to create a SageMaker HyperPod cluster
Grants permission to create a cluster scheduler config
Grants permission to create a CodeRepository
Grants permission to create a compilation job
Grants permission to create a compute quota
Grants permission to create a context
Grants permission to create a data quality job definition
Grants permission to create a device fleet
Grants permission to create a Domain for SageMaker Studio
Grants permission to create an edge deployment plan
Grants permission to create an edge deployment stage
Grants permission to create an edge packaging job
Grants permission to create an endpoint using the endpoint configuration specified in the request
Grants permission to create an endpoint configuration that can be deployed using Amazon SageMaker hosting services
Grants permission to create an experiment
Grants permission to create a feature group
Grants permission to create a flow definition, which defines settings for a human workflow
Grants permission to create a hub
Grants permission to generate S3 presigned URLs with GetObject permission for accessing model artifacts
Grants permission to create hub content reference
Grants permission to define the settings you will use for the human review workflow user interface
Grants permission to create a hyper parameter tuning job that can be deployed using Amazon SageMaker
Grants permission to create a SageMaker Image
Grants permission to create a SageMaker ImageVersion
Grants permission to create an inference component on an endpoint
Grants permission to create an inference experiment
Grants permission to create an inference recommendations job
Grants permission to create a SageMaker model customization job
Grants permission to start a labeling job. A labeling job takes unlabeled data in and produces labeled data as output, which can be used for training SageMaker models
Grants permission to create a lineage group policy
Grants permission to create an MLflow app
Grants permission to create an MLflow tracking server
Grants permission to create a model in Amazon SageMaker. In the request, you specify a name for the model and describe one or more containers
Grants permission to create a model bias job definition
Grants permission to create a model card
Grants permission to create an export job for a model card
Grants permission to create a model explainability job definition
Grants permission to create a ModelPackage
Grants permission to create a ModelPackageGroup
Grants permission to create a model quality job definition
Grants permission to create a monitoring schedule
Grants permission to create an Amazon SageMaker notebook instance. A notebook instance is an Amazon EC2 instance running on a Jupyter Notebook
Grants permission to create a notebook instance lifecycle configuration that can be deployed using Amazon SageMaker
Grants permission to create an optimization job
Grants permission to create an Amazon SageMaker Partner AI App
Grants permission to return a URL that you can use from your browser to connect to the Amazon SageMaker Partner AI App
Grants permission to create a pipeline
Grants permission to return a URL that you can use from your browser to connect to the Domain as a specified UserProfile when AuthMode is 'IAM'
Grants permission to return a URL that you can use from your browser to connect to the MLflow app
Grants permission to return a URL that you can use from your browser to connect to the MLflow tracking server
Grants permission to create a URL that you can use from your browser to connect to the Notebook Instance
Grants permission to start a processing job. After processing completes, Amazon SageMaker saves the resulting artifacts and other optional output to an Amazon S3 location that you specify
Grants permission to create a Project
Grants permission to create a reserved capacity
Grants permission to create a shared model in a SageMaker Studio application
Grants permission to create a Space for a SageMaker Domain
Grants permission to create a Studio Lifecycle Configuration that can be deployed using Amazon SageMaker
Grants permission to start a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts and other optional output to an Amazon S3 location that you specify
Grants permission to create a training plan that allocates resources for scheduling workloads within a specified time range
Grants permission to start a transform job. After the results are obtained, Amazon SageMaker saves them to an Amazon S3 location that you specify
Grants permission to create a trial
Grants permission to create a trial component
Grants permission to create a UserProfile for a SageMaker Domain
Grants permission to create a workforce
Grants permission to create a workteam
Grants permission to delete an action
Grants permission to delete an AI benchmark job
Grants permission to delete an AI recommendation job
Grants permission to delete an AI workload configuration
Grants permission to delete an algorithm
Grants permission to delete an App
Grants permission to delete an AppImageConfig
Grants permission to delete an artifact
Grants permission to delete the association from a lineage entity (artifact, context, action, experiment, experiment-trial-component) to another
Grants permission to delete a SageMaker HyperPod cluster
Grants permission to delete a cluster scheduler config
Grants permission to delete a CodeRepository
Grants permission to delete a compilation job
Grants permission to delete a compute quota
Grants permission to delete a context
Grants permission to delete the data quality job definition created using the CreateDataQualityJobDefinition API
Grants permission to delete a device fleet
Grants permission to delete a Domain
Grants permission to delete an edge deployment plan
Grants permission to delete an edge deployment stage
Grants permission to delete an endpoint. Amazon SageMaker frees up all the resources that were deployed when the endpoint was created
Grants permission to delete the endpoint configuration created using the CreateEndpointConfig API. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete any endpoints created using the configuration
Grants permission to delete an experiment
Grants permission to delete a feature group
Grants permission to delete the specified flow definition
Grants permission to delete hubs
Grants permission to delete hub content
Grants permission to delete hub content reference
Grants permission to delete a specified human loop
Grants permission to delete the specified human task user interface (worker task template)
Grants permission to delete a hyper parameter tuning job
Grants permission to delete a SageMaker Image
Grants permission to delete a SageMaker ImageVersion
Grants permission to delete an inference component. Amazon SageMaker frees up the resources that were reserved when the inference component was created
Grants permission to delete an inference experiment
Grants permission to delete a SageMaker model customization job
Grants permission to delete a lineage group policy
Grants permission to delete an MLflow app
Grants permission to delete an MLflow tracking server
Grants permission to delete a model created using the CreateModel API. The DeleteModel API deletes only the model entry in Amazon SageMaker that you created by calling the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model
Grants permission to delete the model bias job definition created using the CreateModelBiasJobDefinition API
Grants permission to delete a model card
Grants permission to delete the model explainability job definition created using the CreateModelExplainabilityJobDefinition API
Grants permission to delete a ModelPackage
Grants permission to delete a ModelPackageGroup
Grants permission to delete a ModelPackageGroup policy
Grants permission to delete the model quality job definition created using the CreateModelQualityJobDefinition API
Grants permission to delete a monitoring schedule
Grants permission to delete a Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API
Grants permission to delete a notebook instance lifecycle configuration
Grants permission to delete an optimization job
Grants permission to delete an Amazon SageMaker Partner AI App
Grants permission to delete a pipeline
Grants permission to delete a processing job
Grants permission to delete a project
Grants permission to delete a record from a feature group
Grants AWS Resource Access Manager permission to delete a resource policy on a SageMaker resource that supports cross-account sharing
Grants permission to delete a Space
Grants permission to delete a Studio Lifecycle Configuration
Grants permission to delete the specified set of tags from an Amazon SageMaker resource
Grants permission to delete a training job
Grants permission to delete a trial
Grants permission to delete a trial component
Grants permission to delete a UserProfile
Grants permission to delete a workforce
Grants permission to delete a workteam
Grants permission to deploy a model in hub to an endpoint
Grants permission to deregister a set of devices
Grants permission to get information about an action
Grants permission to describe an AI benchmark job
Grants permission to describe an AI recommendation job
Grants permission to describe an AI workload configuration
Grants permission to describe an algorithm
Grants permission to describe an App
Grants permission to describe an AppImageConfig
Grants permission to get information about an artifact
Grants permission to describe an AutoML job that was created via the CreateAutoMLJob API
Grants permission to describe an AutoML job that was created via the CreateAutoMLJobV2 API
Grants permission to return information about a SageMaker HyperPod cluster
Grants permission to return information about an Event within a SageMaker HyperPod cluster
Grants permission to get information about the inference operator for a SageMaker HyperPod cluster
Grants permission to return information about a SageMaker HyperPod cluster node
Grants permission to get information about a cluster scheduler config
Grants permission to describe a CodeRepository
Grants permission to return information about a compilation job
Grants permission to get information about a compute quota
Grants permission to get information about a context
Grants permission to return information about a data quality job definition
Grants permission to access information about a device
Grants permission to access information about a device fleet
Grants permission to describe a Domain
Grants permission to access information about an edge deployment plan
Grants permission to access information about an edge packaging job
Grants permission to return the description of an endpoint
Grants permission to return the description of an endpoint configuration, which was created using the CreateEndpointConfig API
Grants permission to return information about an experiment
Grants permission to return information about a feature group
Grants permission to return information about a feature metadata
Grants permission to return information about the specified flow definition
Grants permission to describe hubs
Grants permission to describe hub content
Grants permission to return information about the specified human loop
Grants permission to return detailed information about the specified human review workflow user interface
Grants permission to describe a hyper parameter tuning job that was created via the CreateHyperParameterTuningJob API
Grants permission to return information about a SageMaker Image
Grants permission to return information about a SageMaker ImageVersion
Grants permission to return the description of an inference component
Grants permission to get information about an inference experiment
Grants permission to get information about an inference recommendations job
Grants permission to return information about a SageMaker model customization job
Grants permission to return information about a job schema version for a particular JobCategory for the CreateJob API
Grants permission to return information about a labeling job
Grants permission to describe a lineage group
Grants permission to get information about an MLflow app
Grants permission to get information about an MLflow tracking server
Grants permission to describe a model that you created using the CreateModel API
Grants permission to return information about a model bias job definition
Grants permission to get information about a model card
Grants permission to get information about a model card export job
Grants permission to return information about a model explainability job definition
Grants permission to describe a ModelPackage
Grants permission to describe a ModelPackageGroup
Grants permission to return information about a model quality job definition
Grants permission to return information about a monitoring schedule
Grants permission to return information about a notebook instance
Grants permission to describe a notebook instance lifecycle configuration that was created via the CreateNotebookInstanceLifecycleConfig API
Grants permission to return information about an optimization job
Grants permission to describe an Amazon SageMaker Partner AI App
Grants permission to get information about a pipeline
Grants permission to get the pipeline definition for a pipeline execution
Grants permission to get information about a pipeline execution
Grants permission to return information about a processing job
Grants permission to describe a project
Grants permission to return information about a specified Reserved Capacity
Grants permission to describe a shared model in a SageMaker Studio application
Grants permission to describe a Space
Grants permission to describe a Studio Lifecycle Configuration
Grants permission to return information about a subscribed workteam
Grants permission to return information about a training job
Grants permission to return information about a specified training plan
Grants permission to return information about a transform job
Grants permission to return information about a trial
Grants permission to return information about a trial component
Grants permission to describe a UserProfile
Grants permission to return information about a workforce
Grants permission to return information about a workteam
Grants permission to detach an Amazon EBS volume from a SageMaker HyperPod cluster node
Grants permission to disable a SageMaker Service Catalog Portfolio
Grants permission to disassociate a trial component from a trial
Grants permission to enable a SageMaker Service Catalog Portfolio
Grants permission to get deployment plan for device
Grants permission to access a summary of the devices in a device fleet
Grants permission to get device registration. After you deploy a model onto edge devices this api is used to get current device registration
Grants permission to retreive a lineage group policy
Grants permission to get a ModelPackageGroup policy
Grants permission to get a record from a feature group
Grants AWS Resource Access Manager permission to retrieve a resource policy on a SageMaker resource that supports cross-account sharing
Grants permission to get a SageMaker Service Catalog Portfolio
Grants permission to get a scaling policy configuration recommendation
Grants permission to get search suggestions when provided with a keyword
Grants permission to import hub content
Grants permission to invoke an endpoint. After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint
Grants permission to get inferences from the hosted model at the specified endpoint in an asynchronous manner
Grants permission to get the inference response as a stream from the specified endpoint
Grants permission to list actions
Grants permission to list AI benchmark jobs
Grants permission to list AI recommendation jobs
Grants permission to list AI workload configurations
Grants permission to list Algorithms
Grants permission to list Aliases that belong to a SageMaker Image or Sagemaker ImageVersion
Grants permission to list the AppImageConfigs in your account
Grants permission to list the Apps in your account
Grants permission to list artifacts
Grants permission to list associations
Grants permission to list AutoML jobs
Grants permission to lists candidates for an AutoML job
Grants permission to list events within a SageMaker HyperPod cluster
Grants permission to list nodes within a SageMaker HyperPod cluster
Grants permission to list SageMaker HyperPod clusters
Grants permission to list cluster scheduler configs
Grants permission to list code repositories
Grants permission to list compilation jobs
Grants permission to list compute quotas
Grants permission to list contexts
Grants permission to list data quality job definitions
Grants permission to list device fleets
Grants permission to list devices
Grants permission to list the Domains in your account
Grants permission to list edge deployment plans
Grants permission to list edge packaging jobs
Grants permission to list endpoint configurations
Grants permission to list endpoints
Grants permission to list experiments
Grants permission to list feature groups
Grants permission to return summary information about flow definitions, given the specified parameters
Grants permission to list newest versions of hub content
Grants permission to list all versions of hub content
Grants permission to list hubs
Grants permission to return summary information about human loops, given the specified parameters
Grants permission to return summary information about human review workflow user interfaces, given the specified parameters
Grants permission to list hyper parameter tuning jobs
Grants permission to list SageMaker Images in your account
Grants permission to list ImageVersions that belong to a SageMaker Image
Grants permission to list inference components
Grants permission to list inference experiments
Grants permission to list inference recommendations jobs
Grants permission to list inference recommendations job steps
Grants permission to list SageMaker model customization jobs
Grants permission to list job schema versions for a particular JobCategory for the CreateJob API
Grants permission to list labeling jobs
Grants permission to list labeling jobs for workteam
Grants permission to list lineage groups
Grants permission to list SageMaker MLflow Apps in your account
Grants permission to list MLflow tracking servers
Grants permission to list model bias job definitions
Grants permission to list export jobs for a model card
Grants permission to list model cards
Grants permission to list versions of a model card
Grants permission to list model explainability job definitions
Grants permission to list model metadata for inference recommendations jobs
Grants permission to list ModelPackageGroups
Grants permission to list ModelPackages
Grants permission to list model quality job definitions
Grants permission to list the models created with the CreateModel API
Grants permission to list the history of a monitoring alert
Grants permission to list monitoring alerts
Grants permission to list monitoring executions
Grants permission to list monitoring schedules
Grants permission to list the notebook instance lifecycle configurations that can be deployed using Amazon SageMaker
Grants permission to list the Amazon SageMaker notebook instances in the requester's account in an AWS Region
Grants permission to list optimization jobs
Grants permission to list the Amazon SageMaker Partner AI Apps in your account
Grants permission to list executions for a pipeline
Grants permission to list steps for a pipeline execution
Grants permission to list parameters for a pipeline execution
Grants permission to list pipelines
Grants permission to list versions of a pipeline
Grants permission to list processing jobs
Grants permission to list Projects
Grants permission to list record identifiers from a feature group
Grants permission to list resource catalogs
Grants permission to list shared model events
Grants permission to list shared models
Grants permission to list shared model versions
Grants permission to list the Spaces in your account
Grants permission to list stage devices
Grants permission to list the Studio Lifecycle Configurations that can be deployed using Amazon SageMaker
Grants permission to list subscribed workteams
Grants permission to list the tag set associated with the specified resource
Grants permission to list training jobs
Grants permission to list training jobs for a hyper parameter tuning job
Grants permission to list all the training plans that have been created in a specified account
Grants permission to list transform jobs
Grants permission to list trial components
Grants permission to list trials
Grants permission to list all UltraServers in a specified Reserved Capacity
Grants permission to list the UserProfiles in your account
Grants permission to list workforces
Grants permission to list workteams
Grants permission to put a lineage group policy
Grants permission to put a ModelPackageGroup policy
Grants permission to put a record to a feature group
Grants AWS Resource Access Manager permission to create a resource policy on a SageMaker resource that supports cross-account sharing
Grants permission to explore the lineage graph
Grants permission to register a set of devices
Grants permission to render a UI template used for a human annotation task
Grants permission to retry a pipeline execution
Grants permission to invoke a sample request against a job
Grants permission to invoke a streaming sample request against a job
Grants permission to search for SageMaker objects
Grants permissions to search for the available training plan offerings that best match specified capacity requirements
Grants permission to publish heartbeat data from devices. After you deploy a model onto edge devices this api is used to report device status
Grants permission to fail a pending callback step
Grants permission to succeed a pending callback step
Grants permission to send a shared model event
Grants permission to start deep health checks for a SageMaker Hyperpod cluster
Grants permission to start an edge deployment stage
Grants permission to start a human loop
Grants permission to start an inference experiment
Grants permission to start an MLfLow tracking server
Grants permission to start a monitoring schedule
Grants permission to start a notebook instance. This launches an EC2 instance with the latest version of the libraries and attaches your EBS volume
Grants permission to start a pipeline execution
Grants permission to start a remote session for a SageMaker space
Grants permission to stop an AI benchmark job
Grants permission to stop an AI recommendation job
Grants permission to stop a running AutoML job
Grants permission to stop a compilation job
Grants permission to stop an edge deployment stage
Grants permission to stop an edge packaging job
Grants permission to stop a specified human loop
Grants permission to stop a running hyper parameter tuning job create via the CreateHyperParameterTuningJob
Grants permission to stop an inference experiment
Grants permission to stop an inference recommendations job
Grants permission to stop a SageMaker model customization job
Grants permission to stop a labeling job. Any labels already generated will be exported before stopping
Grants permission to stop an MLflow tracking server
Grants permission to stop a monitoring schedule
Grants permission to stop a notebook instance. This terminates the EC2 instance. Before terminating the instance, Amazon SageMaker disconnects the EBS volume from it. Amazon SageMaker preserves the EBS volume
Grants permission to stop an optimization job
Grants permission to stop a pipeline execution
Grants permission to stop a processing job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds
Grants permission to stop a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds
Grants permission to stop a transform job. When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped
Grants permission to train a model in hub
Grants permission to update an action
Grants permission to update an AppImageConfig
Grants permission to update an artifact
Grants permission to update a SageMaker HyperPod cluster
Grants permission to update the inference operator for a SageMaker HyperPod cluster
Grants permission to update a cluster scheduler config
Grants permission to update platform software for a SageMaker HyperPod cluster
Grants permission to update a CodeRepository
Grants permission to update a compute quota
Grants permission to update a context
Grants permission to update a device fleet
Grants permission to update a set of devices
Grants permission to update a Domain
Grants permission to update an endpoint to use the endpoint configuration specified in the request
Grants permission to update variant weight, capacity, or both of one or more variants associated with an endpoint
Grants permission to update an experiment
Grants permission to update a feature group
Grants permission to update a feature metadata
Grants permission to update hubs
Grants permission to update hub content
Grants permission to update hub content reference
Grants permission to update the properties of a SageMaker Image
Grants permission to update the properties of a SageMaker ImageVersion
Grants permission to update an inference component to use the specification and configurations specified in the request
Grants permission to update the runtime config of a given inference component
Grants permission to update an inference experiment
Grants permission to update an MLflow app
Grants permission to update an MLflow tracking server
Grants permission to update a model card
Grants permission to update a ModelPackage
Grants permission to update a monitoring alert
Grants permission to update a monitoring schedule
Grants permission to update a notebook instance. Notebook instance updates include upgrading or downgrading the EC2 instance used for your notebook instance to accommodate changes in your workload requirements
Grants permission to updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API
Grants permission to update an Amazon SageMaker Partner AI App
Grants permission to update a pipeline
Grants permission to update a pipeline execution
Grants permission to update a pipeline version
Grants permission to update a Project
Grants permission to submit reward scores for a trajectory in a job
Grants permission to update a shared model
Grants permission to update a Space
Grants permission to update a training job
Grants permission to update a trial
Grants permission to update a trial component
Grants permission to update a UserProfile
Grants permission to update a workforce
Grants permission to update a workteam