Persona reference
Amazon SageMaker Role Manager provides suggested permissions for a number of ML personas. These include user execution roles for common ML practitioner responsibilities as well as service execution roles for common AWS service interactions needed to work with SageMaker.
Each persona has suggested permissions in the form of selected ML activities. For information on predefined ML activities and their permissions, see ML activity reference.
Data scientist persona
Use this persona to configure permissions to perform general machine learning development and experimentation in a SageMaker environment. This persona includes the following preselected ML activities:
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Run Studio Classic Applications
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Manage ML Jobs
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Manage Models
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Manage AWS Glue Tables
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Canvas AI Services
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Canvas MLOps
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Canvas Kendra Access
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Use MLflow
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Access required to AWS Services for MLflow
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Run Studio EMR Serverless Applications
MLOps persona
Choose this persona to configure permissions for operational activities. This persona includes the following preselected ML activities:
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Run Studio Classic Applications
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Manage Models
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Manage Pipelines
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Search and visualize experiments
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Amazon S3 Full Access
SageMaker compute persona
Note
We recommend that you first use the role manager to create a SageMaker Compute Role so that SageMaker compute resources can perform tasks such as training and inference. Use the SageMaker Compute Role persona to create this role with the role manager. After creating a SageMaker Compute Role, take note of its ARN for future use.
This persona includes the following preselected ML activity:
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Access Required AWS Services
