Actions, resources, and condition keys for Amazon Machine Learning

Amazon Machine Learning (service prefix: machinelearning) provides the following service-specific resources, actions, and condition context keys for use in IAM permission policies.

References:

• Learn how to configure this service.

• View a list of the API operations available for this service.

• Learn how to secure this service and its resources by using IAM permission policies.

Actions defined by Amazon Machine Learning

You can specify the following actions in the Action element of an IAM policy statement. Use policies to grant permissions to perform an operation in AWS. When you use an action in a policy, you usually allow or deny access to the API operation or CLI command with the same name. However, in some cases, a single action controls access to more than one operation. Alternatively, some operations require several different actions.

The Resource types column indicates whether each action supports resource-level permissions. If there is no value for this column, you must specify all resources ("*") in the Resource element of your policy statement. If the column includes a resource type, then you can specify an ARN of that type in a statement with that action. Required resources are indicated in the table with an asterisk (*). If you specify a resource-level permission ARN in a statement using this action, then it must be of this type. Some actions support multiple resource types. If the resource type is optional (not indicated as required), then you can choose to use one but not the other.

For details about the columns in the following table, see The actions table.

Actions	Description	Access level	Resource types (*required)	Condition keys	Dependent actions
AddTags	Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value	Tagging	batchprediction
			datasource
			evaluation
			mlmodel
CreateBatchPrediction	Generates predictions for a group of observations	Write	batchprediction*
			datasource*
			mlmodel*
CreateDataSourceFromRDS	Creates a DataSource object from an Amazon RDS	Write	datasource*
CreateDataSourceFromRedshift	Creates a DataSource from a database hosted on an Amazon Redshift cluster	Write	datasource*
CreateDataSourceFromS3	Creates a DataSource object from S3	Write	datasource*
CreateEvaluation	Creates a new Evaluation of an MLModel	Write	datasource*
			evaluation*
			mlmodel*
CreateMLModel	Creates a new MLModel	Write	datasource*
			mlmodel*
CreateRealtimeEndpoint	Creates a real-time endpoint for the MLModel	Write	mlmodel*
DeleteBatchPrediction	Assigns the DELETED status to a BatchPrediction, rendering it unusable	Write	batchprediction*
DeleteDataSource	Assigns the DELETED status to a DataSource, rendering it unusable	Write	datasource*
DeleteEvaluation	Assigns the DELETED status to an Evaluation, rendering it unusable	Write	evaluation*
DeleteMLModel	Assigns the DELETED status to an MLModel, rendering it unusable	Write	mlmodel*
DeleteRealtimeEndpoint	Deletes a real time endpoint of an MLModel	Write	mlmodel*
DeleteTags	Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags	Tagging	batchprediction
			datasource
			evaluation
			mlmodel
DescribeBatchPredictions	Returns a list of BatchPrediction operations that match the search criteria in the request	List
DescribeDataSources	Returns a list of DataSource that match the search criteria in the request	List
DescribeEvaluations	Returns a list of DescribeEvaluations that match the search criteria in the request	List
DescribeMLModels	Returns a list of MLModel that match the search criteria in the request	List
DescribeTags	Describes one or more of the tags for your Amazon ML object	List	batchprediction
			datasource
			evaluation
			mlmodel
GetBatchPrediction	Returns a BatchPrediction that includes detailed metadata, status, and data file information	Read	batchprediction*
GetDataSource	Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource	Read	datasource*
GetEvaluation	Returns an Evaluation that includes metadata as well as the current status of the Evaluation	Read	datasource*
GetMLModel	Returns an MLModel that includes detailed metadata, and data source information as well as the current status of the MLModel	Read	mlmodel*
Predict	Generates a prediction for the observation using the specified ML Model	Write	mlmodel*
UpdateBatchPrediction	Updates the BatchPredictionName of a BatchPrediction	Write	batchprediction*
UpdateDataSource	Updates the DataSourceName of a DataSource	Write	datasource*
UpdateEvaluation	Updates the EvaluationName of an Evaluation	Write	evaluation*
UpdateMLModel	Updates the MLModelName and the ScoreThreshold of an MLModel	Write	mlmodel*

Resource types defined by Amazon Machine Learning

The following resource types are defined by this service and can be used in the Resource element of IAM permission policy statements. Each action in the Actions table identifies the resource types that can be specified with that action. A resource type can also define which condition keys you can include in a policy. These keys are displayed in the last column of the table. For details about the columns in the following table, see The resource types table.

Resource types	ARN	Condition keys
batchprediction	arn:${Partition}:machinelearning:${Region}:${Account}:batchprediction/${BatchPredictionId}
datasource	arn:${Partition}:machinelearning:${Region}:${Account}:datasource/${DatasourceId}
evaluation	arn:${Partition}:machinelearning:${Region}:${Account}:evaluation/${EvaluationId}
mlmodel	arn:${Partition}:machinelearning:${Region}:${Account}:mlmodel/${MlModelId}

Condition keys for Amazon Machine Learning

Machine Learning has no service-specific context keys that can be used in the Condition element of policy statements. For the list of the global context keys that are available to all services, see Available keys for conditions.