Implement user-level access control for multi-tenant ML platforms on Amazon SageMaker AI

This article provides a comprehensive guide to implementing user-level access control for multi-tenant machine learning platforms on Amazon SageMaker AI using advanced IAM techniques.

• Introduces attribute-based access control (ABAC) to manage permissions across shared ML environments
• Demonstrates how to use source identity and IAM policy variables to create granular access controls
• Provides detailed examples of implementing access controls for:
  • SageMaker training jobs
  • Amazon S3 buckets
  • Secrets Manager
  • Amazon EMR clusters
  • AWS Glue Data Catalog
• Highlights best practices for secure multi-tenant ML platforms, including:
  • Consistent naming conventions
  • Least privilege access
  • Regular access auditing

The solution enables organizations to implement fine-grained access controls without creating numerous IAM roles, improving security and operational efficiency in shared ML environments.