MLflow Documentation

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

• Tracking experiments to record and compare parameters and results (MLflow Tracking).

• Packaging ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production (MLflow Projects).

• Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).

• Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).

MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.

Get started using the Quickstart: Install MLflow, instrument code & view results in minutes or by reading about the key concepts.

• What is MLflow?
• Quickstart: Install MLflow, instrument code & view results in minutes
• Quickstart: Compare runs, choose a model, and deploy it to a REST API
• Tutorials and Examples
• Concepts
• MLflow Tracking
• MLflow LLM Tracking
• MLflow Projects
• MLflow Models
• MLflow Model Registry
• MLflow Recipes
• MLflow AI Gateway (Experimental)
• Prompt Engineering UI (Experimental)
• MLflow Plugins
• MLflow Authentication
• Command-Line Interface
• Search Runs
• Search Experiments
• Python API
• R API
• Java API
• REST API
• Official MLflow Docker Image
• Community Model Flavors