MLflow
MLflow is an open-source platform for managing the machine learning lifecycle, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. It helps data scientists and engineers organize ML projects from development to production.
AI companies need standardized tools to manage complex ML workflows across teams, ensuring reproducibility, collaboration, and efficient deployment. MLflow has become the industry standard for MLOps, especially in companies like Databricks that created and heavily use it.
🎓 Courses
Managing Machine Learning Projects with MLflow
by Andrew Ng, Robert Crowe
This official DeepLearning.AI course provides hands-on experience with MLflow's core components for experiment tracking and model management.
MLflow: A Complete Guide
by Sundog Education by Frank Kane
Covers MLflow comprehensively from basics to advanced deployment scenarios with practical examples.
MLOps with MLflow
by Data Science Dojo
Free video series demonstrating MLflow integration into complete MLOps pipelines with real-world examples.
Doing MLOps with Databricks and MLFlow
by Noah Gift
Full video course on logging, registering, versioning, and deploying MLflow models on Databricks
📖 Books
MLflow in Action
Saeed Aghabozorgi · 2024
Comprehensive guide covering MLflow's latest features for experiment tracking, model registry, and production deployment.
Machine Learning Engineering with Python
Andrew P. McMahon · 2023
Contains dedicated chapters on MLflow for model management and deployment within complete ML engineering workflows.
🛠️ Tutorials & Guides
MLflow Tutorials and Examples
Official tutorials covering basic tracking to advanced deployment scenarios with up-to-date code examples.
Getting Started with MLflow
Practical introduction from MLflow's creators with real implementation patterns used at Databricks.
MLflow Model Registry Tutorial
Step-by-step guide to using MLflow's model registry for versioning and staging models.
Building End-to-End ML Pipelines with MLflow
Shows how to integrate MLflow into complete production ML pipelines with best practices.
Learning resources last updated: April 13, 2026