Production Governance and MLOps on Databricks

This intermediate course provides a practical, hands-on exploration of Databricks Governance, focusing on the essential tools and workflows for managing and securing your data lakehouse. You will learn to navigate and control access to your data assets using Unity Catalog, the foundation of Databricks governance. The course covers the core hierarchy of metastores, catalogs, schemas, and tables, and teaches you how to manage them programmatically using the Databricks Python SDK, CLI, and VS Code extension. Beyond foundational access control, you will master the skills to implement modern CI/CD and MLOps practices directly within the Databricks environment. You'll learn to integrate Databricks Repos with GitHub, automate notebook testing and deployment with GitHub Actions, and understand the architectural considerations for managing machine learning models in production. Finally, you will explore how to ensure ongoing data reliability by setting up and understanding Lakehouse Monitoring for data quality and freshness. This course is unique because it moves beyond theory, demonstrating how to apply these governance concepts with the actual tools and code used by data professionals. By the end, you'll be equipped to build, deploy, and monitor secure and reliable data pipelines and AI applications on the Databricks platform