Governing the ML lifecycle at scale: Centralized observability with Amazon SageMaker and Amazon CloudWatch

This article discusses how to set up centralized observability for machine learning (ML) workloads across multiple AWS accounts using Amazon SageMaker and Amazon CloudWatch.

Specifically, the article covers:

• Deploying an ML model and setting up SageMaker Model Monitor for performance evaluation
• Enabling CloudWatch cross-account observability to consolidate metrics from different accounts
• Creating unified CloudWatch dashboards to monitor metrics like accuracy, AUC, CPU usage across accounts
• Configuring centralized CloudTrail logging for API activity monitoring across SageMaker environments
• Conclusion highlighting the benefits of centralized observability for ML governance at scale