Summary

This chapter discussed various aspects of ML model governance, including documentation, versioning, monitoring, auditing, compliance, operationalization, and continuous improvement. We then explored some industry-specific and region-specific regulations, such as HIPAA for healthcare, SOX for finance, GDPR (EU), and CCPA (California).

Next, we focused on the Google Cloud Architecture Framework and how to apply its pillars—Operational excellence, Security, privacy and compliance, Reliability, Cost optimization, and Performance efficiency—to the various stages of the ML life cycle. We dived deep into each pillar, detailing its relevance across different phases, from data collection and preparation to model evaluation and deployment. This included important concepts, such as cost-efficient model deployment, enhancing security throughout the model life cycle, and maintaining high reliability and performance standards. Overall, this chapter covered many factors related...