Welcome to MLflow for Beginners

This book is designed to take you from a complete beginner in MLOps to an advanced practitioner using MLflow.

What is MLflow?¶

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:

1. Tracking: Record and query experiments: code, data, config, and results.

2. Projects: Package data science code in a format to reproduce runs on any platform.

3. Models: Deploy machine learning models in diverse serving environments.

4. Model Registry: Store, annotate, discover, and manage models in a central repository.

What will you learn?¶

• Setup: Professional environment management with uv.

• Foundations: Autologging, nested runs, and managing complex artifacts.

• Intermediate: Model signatures, custom PyFunc wrappers, and production-ready model versions.

• Advanced: Docker-based serving and full LLMOps observability with Tracing.

Let’s dive in!

• MLflow for Beginners
• Getting Started
  • Setting Up a Professional MLOps Environment
• Phase 1 - Foundations
  • Deep Dive into Experiment Tracking
  • Navigating the MLflow UI
  • Reproducible Packaging with MLflow Projects
• Phase 2 - Intermediate
  • Model Packaging and Signatures
  • The Model Registry & Lifecycle
  • Standardizing Workflows with MLflow Recipes
• Phase 3 - Advanced
  • Serving Models for Production
  • Advanced LLMOps with MLflow Tracing
  • Pipeline Orchestration & Quality Gates
• Phase 4 - Mastery
  • Cloud Integration & Security
  • Data Versioning & Lineage
  • Monitoring & Drift Detection