MLflow

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, developed by Databricks. It provides tools for experiment tracking, model packaging, deployment, and a central model registry to streamline ML workflows. It is designed to work with any ML library, language, or existing code, making it highly flexible for data science teams.

Also known as: ML Flow, MLflow Platform, Machine Learning Flow, MLflow Tracking, MLflow Projects

Why learn MLflow?

Developers should learn MLflow when building production-grade machine learning systems that require reproducibility, collaboration, and scalability. It is essential for tracking experiments across multiple runs, managing model versions, and deploying models consistently in environments like cloud platforms or on-premises servers. Use cases include A/B testing, model governance, and automating ML pipelines in enterprise settings.

Learning Resources

• docs: MLflow Documentation
• tutorial: MLflow Tutorials on Databricks
• course: MLflow: A Tool for Managing the Machine Learning Lifecycle (Coursera)
• video: Getting Started with MLflow - YouTube
• book: Machine Learning Engineering with MLflow