Learn MLOps with MLflow and Databricks – Full Course for Machine Learning Engineers

This end-to-end course provides a deep dive into MLflow, the industry standard for managing the machine learning life cycle from local experimentation to production-ready deployment. You will master essential MLOps and LLM ops workflows, including experiment tracking, model versioning, prompt management, and systematic evaluation using custom scorers. Finally, the guide demonstrates professional integration with Databricks and Hugging Face to build reproducible, scalable, and observable ML systems for real-world enterprise environments. ✏️ Course from @datageekrj ❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp Contents Part 1: The Theory & Need for MLOps** 00:00 Introduction to MLflow and the Machine Learning Lifecycle 02:22 Why ML Systems Need Experiment Tracking 03:31 The Problem with Jupyter Notebook Scaling 06:22 Probabilistic vs. Deterministic Software Development 07:14 The