Governing ML lifecycle at scale: Best practices to set up cost and usage visibility of ML workloads in multi-account environments

The article discusses best practices for setting up cost and usage visibility for machine learning (ML) workloads in multi-account environments on AWS.

Specifically, the article covers:

• Implementing a tagging strategy for resources, including cost allocation, automation, access control, technical, compliance, and business tags
• Enforcing the tagging strategy through proactive and reactive governance approaches using AWS services like CloudFormation, Service Catalog, Organizations, Config, and Resource Groups
• Monitoring resources and costs using AWS Cost Explorer, Budgets, and Data Exports
• Cost allocation and visualization techniques for ML workloads in multi-account setups
• Conclusion highlighting the importance of a comprehensive tagging strategy for cost and usage visibility of ML workloads