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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:
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
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2026/03/05
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