Governing the ML lifecycle at scale, Part 1: A framework for architecting ML workloads using Amazon SageMaker

This article presents a framework for governing the machine learning (ML) lifecycle at scale using AWS services like Amazon SageMaker. It helps organizations implement secure, scalable, and reliable ML environments with embedded security and governance controls.

Specifically, the article covers:

• Solution overview of the framework for governing ML lifecycle at scale
• Reference architecture modules including multi-account foundations, data lake foundations, ML platform services, ML use case development, ML operations, centralized feature store, logging and observability, and cost and reporting
• Reference account structure for organizing accounts by OUs like security, infrastructure, workloads, and deployments
• AWS environment controls like preventive, detective, and proactive controls
• Interactions between ML platform services, ML use cases, and ML operations with different personas
• Conclusion highlighting the holistic approach of the framework