Databricks MLOps and Production Governance Guide

Moving machine learning models from development to production requires more than just good code—it demands robust governance and structured operations. This text-based course guides you through the foundational concepts of MLOps and data governance specifically tailored for the Databricks ecosystem. By reading through our structured explanations and analyzing real-world configuration examples, you will understand how to secure data assets, track model experiments, and maintain compliance throughout the machine learning lifecycle. What you'll learn: - Understand foundational MLOps principles and data governance frameworks. - Configure Unity Catalog to manage permissions and secure data lineage. - Track machine learning experiments and register models using MLflow. - Apply modern model monitoring techniques to detect drift and maintain performance. - Implement basic CI/CD patterns for automated machine learning pipelines. The course starts with essential terminology and the core pillars of governance before moving into step-by-step written walkthroughs on MLflow, Unity Catalog, and model deployment strategies. It is designed for aspiring data engineers, beginner machine learning practitioners, and technical analysts who want to understand production workflows without needing prior advanced MLOps experience. Start reading today to build a secure and scalable foundation for your machine learning projects on Databricks.