MLflow Documentation

MLflow is an open source platform for managing the end-to-end machine learning lifecycle. It tackles three 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).

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.

Get started using the Quickstart or by reading about the key concepts.

• Quickstart
• Tutorial
• Concepts
• MLflow Tracking
• MLflow Projects
• MLflow Models
• Command-Line Interface
• Python API
• REST API

Warning

The current version of MLflow is an alpha release. This means that APIs and storage formats are subject to breaking change.