Persona reference - Amazon SageMaker
Data scientist personaMLOps personaSageMaker compute persona
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

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 Amazon 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:

  • Run Studio Classic Applications

  • Manage ML Jobs

  • Manage Models

  • Manage Experiments

  • Search and Visualize Experiments

  • Amazon S3 Bucket Access

MLOps persona

Choose this persona to configure permissions for operational activities. This persona includes the following preselected ML activities:

  • Run Studio Classic Applications

  • Manage Models

  • Manage Endpoints

  • Manage Pipelines

  • Search and Visualize Experiments

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:

  • Access Required Amazon Services