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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:
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