Part 3: ML Governance and Deployment

You will learn how to utilize the MLFlow model registry to manage model versioning and transition to production from various stages and use webhooks to set up alerts and monitoring.

This section has the following chapters:

• Chapter 6, Model Versioning and Webhooks
• Chapter 7, Model Deployment Approaches
• Chapter 8, Automating ML Workflows Using Databricks Jobs
• Chapter 9, Model Drift Detection and Retraining
• Chapter 10, Using CI/CD to Automate Model Retraining and Redeployment