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 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: Parameters, Metrics, and Artifacts Part 3: Advanced Model Management 44:43 Logging Media: Visualizing Loss Graphs and Images 48:28 Data Previews: Logging Pandas Tables and Data Frames 52:46 Training Models: Manual vs. Auto Logging with Scikit-Learn 59:01 The Model Registry: Lineage, Versioning, and Aliasing 01:13:36 Deployment Essentials: Understanding Model URIs 01:15:19 Serving Models as Production HTTP Endpoints Part 4: LLM Ops & Prompt Engineering 01:22:42 Introduction to GenAI Ops and managing LLM Prompts 01:25:34 The Prompt Registry: Building and Versioning Templates 01:28:25 Quality Control: Comparing Different Prompt Versions 01:37:43 Integrating MLflow Prompts with the OpenAI API 01:46:14 Systematic Prompt Evaluation Frameworks Part 5: Advanced LLM Evaluation 01:54:39 LLM-as-a-Judge: Correctness and Guideline Scorers 02:00:11 Debugging Results: Understanding AI-Generated Rationales 02:09:00 Coding Custom Scorers for Specific Business Logic 02:13:54 Performance Visualization: Pass/Fail Trends and Comparative Runs Part 6: Databricks & Enterprise MLOps 02:33:44 MLflow in the Enterprise: The Databricks Advantage 02:39:27 Configuring Enterprise Compute and Serverless Clusters 02:51:12 Collaboration: User Management and the Unity Catalog 03:02:57 Registering and Serving Models in Enterprise Environments 03:22:15 Real-world Case Study: Hugging Face Transformer Deployment Part 7: Databricks & Enterprise MLOps 03:38:20 MLflow in the Enterprise: The Databricks Advantage 03:40:00 Setting Up a Databricks Account and Workspace 03:42:30 Configuring Serverless Compute and GPU Clusters 03:46:15 Workspace Notebooks and AI Coding Assistants 03:51:10 Enterprise Collaboration: User Management and Access Identity 04:12:50 Automated Experiment Tracking on Databricks 04:18:20 Implementing Nested Runs for Sub-Hypothesis Testing 04:23:00 The Unity Catalog: Managing Models and Schemas 04:31:40 Registering Models into a Centralized Enterprise Registry 04:34:30 Real-time Model Serving on Databricks 04:41:20 Securing Endpoints with Authentication Tokens Part 8: Advanced Project — Transformer Model Deployment 04:44:40 Real-World Case Study: Deploying Hugging Face Transformers 04:47:45 Environment Setup: Installing PyTorch and Transformers 04:50:40 Downloading and Localizing Embedding Models from Hugging Face 05:00:10 Building a Custom PyFunc Wrapper for Transformer Models 05:04:00 Implementing the Load Context and Predict Logic 05:17:20 Model Versioning and Registration in Unity Catalog 05:21:15 Scaling Production Endpoints and Cold-Start Latency 05:27:15 Final Summary and Industry Workflow Conclusions 🎉 Thanks to our Champion and Sponsor supporters: 👾 @omerhattapoglu1158 👾 @goddardtan 👾 @akihayashi6629 👾 @kikilogsin 👾 @anthonycampbell2148 👾 @tobymiller7790 👾 @rajibdassharma497 👾 @CloudVirtualizationEnthusiast 👾 @adilsoncarlosvianacarlos 👾 @martinmacchia1564 👾 @ulisesmoralez4160 👾 @_Oscar_ 👾 @jedi-or-sith2728 👾 @justinhual1290 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news