Governing the ML lifecycle at scale, Part 2: Multi-account foundations

This article discusses best practices for implementing a multi-account foundation to support and govern machine learning (ML) and analytics workloads at scale on AWS.

Specifically, the article covers:

• Recommended organizational units and account structure for isolating resources and providing cost visibility
• Using AWS Control Tower to implement a baseline landing zone and automate account provisioning
• Securing workloads across accounts using the AWS Security Reference Architecture
• Scaling and sharing ML resources across accounts using AWS Service Catalog
• Creating a hub-and-spoke network architecture using AWS Transit Gateway
• Conclusion highlighting the importance of a multi-account foundation for governing ML workloads