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
- MLflow Tracking
- MLflow LLM Tracking
- MLflow Projects
- MLflow Models
- MLflow Model Registry
- MLflow Recipes
- MLflow Plugins
- Command-Line Interface
- Search Runs
- Search Experiments
- Python API
- R API
- Java API
- REST API
- Official MLflow Docker Image
- Community Model Flavors