MLflow: A Tool for Managing the Machine Learning Lifecycle

MLflow is an open-source platform, purpose-built to assist machine learning practitioners and teams in handling the complexities of the machine learning process. MLflow focuses on the full lifecycle for machine learning projects, ensuring that each phase is manageable, traceable, and reproducible.

MLflow Getting Started Resources

If this is your first time exploring MLflow, the tutorials and guides here are a great place to start. The emphasis in each of these is getting you up to speed as quickly as possible with the basic functionality, terms, APIs, and general best practices of using MLflow in order to enhance your learning in area-specific guides and tutorials.

Learn about the core components of MLflow

Quickstarts

Get Started with MLflow in our 5-minute tutorial

Guides

Learn the core components of MLflow with this in-depth guide to Tracking

GenAI and MLflow

Explore the comprehensive GenAI-focused support in MLflow. From MLflow Deployments for GenAI models to the Prompt Engineering UI and native GenAI-focused MLflow flavors like open-ai, transformers, and sentence-transformers, the tutorials and guides here will help to get you started in leveraging the benefits of these powerful models, services, and applications. You’ll learn how MLflow simplifies both using GenAI models and developing solutions that leverage them. Important tasks such as prompt development, evaluation of prompts, comparison of foundation models, fine-tuning, logging, and deploying production-grade inference servers are all covered by MLflow.

Explore the guides and tutorials below to start your journey!