Administrators of machine learning (ML) workloads are focused on ensuring that users are operating in the most secure manner, striving towards a principal of least privilege design. They have a wide variety of personas to account for, each with their own unique sets of needs, and building the right sets of permissions policies to meet those needs can sometimes be an inhibitor to agility. In this post, we look at how to use Amazon SageMaker Role Manager to quickly build out a set of persona-based roles that can be further customized to your specific requirements in minutes, right on the Amazon SageMaker console.
Role Manager offers predefined personas and ML activities combined with a wizard to streamline your permission generation process, allowing your ML practitioners to perform their responsibilities with the minimal necessary permissions. If you require additional customization, SageMaker Role Manager allows you to specify networking and encryption permissions

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