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

From the creator

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 5 Core Components of an ML Experiment 10:20 Risks of Operating Without Tracking: Reproducibility and Audits Part 2: Local MLflow Implementation 14:32 Local Setup and Virtual Environment Configuration 17:36 Installing MLflow and Starting the Tracking Server 21:14 Creating Your First Experiment and Logging Runs 24:44 Backend Store vs. Artifact Store: Understanding Where Data Lives 31:05 Technical Deep Dive: Exploring the MLflow SQLite Database 37:07 Comprehensive Logging: Parameter

Published 2026-03-05 · Watch on YouTube ↗
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