Validating Models in Production

This chapter explores the methodologies for validating machine learning models in production environments, including the concepts of 'dark canary' and 'shadow' testing. It emphasizes the importance of logging data for performance estimation and outlines strategies for debugging, monitoring, and managing model lifecycle challenges. Additionally, the discussion highlights the need for collaboration among teams to ensure stable model serving and effective alerting systems.