Amazon SageMaker Studio is a web-based integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Each onboarded user in Studio has their own dedicated set of resources, such as compute instances, a home directory on an Amazon Elastic File System (Amazon EFS) volume, and a dedicated AWS Identity and Access Management (IAM) execution role.
One of the most common real-world challenges in setting up user access for Studio is how to manage multiple users, groups, and data science teams for data access and resource isolation.
Many customers implement user management using federated identities with AWS Single Sign-On (AWS SSO) and an external identity provider (IdP), such as Active Directory (AD) or AWS Managed Microsoft AD directory. It’s aligned with the AWS recommended practice of using temporary credentials to access AWS accounts.
An Amazon SageMaker domain supports AWS SSO and

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