MLflow Project (Linux Foundation)
Deliver High-Quality AI, Fast. MLflow is an open-source AI engineering platform for managing the end-to-end ML and GenAI lifecycle.
At a Glance
- Enterprise AI Teams
- Data Science Researchers
- MLOps Engineers
- AI Startups
AI Tools by MLflow Project (Linux Foundation)
(1)
MLflow
Open Source AI Lifecycle Platform
Discussions
No discussions yet
Be the first to start a discussion about MLflow Project (Linux Foundation)
Latest News
Products & Services
API and UI for logging parameters, code versions, metrics, and output files.
Standard format for packaging reusable data science code.
Standard format for packaging machine learning models to be used in various downstream tools.
Centralized model store, set of APIs, and UI to collaboratively manage the full lifecycle of an MLflow Model.
Market Position
Leading vendor-neutral open-source platform for ML and AI engineering, competing with proprietary tools like Weights & Biases and Comet.ml.
Leadership
Founders
Matei Zaharia
Co-founder and CTO of Databricks, creator of Apache Spark and MLflow. Assistant Professor at Stanford University.
Databricks Team
The company that originally created and open-sourced MLflow in 2018 before donating it to the Linux Foundation.
Executive Team
Matei Zaharia
Creator & TSC Member
CTO of Databricks and lead visionary for the project.
Patrick Wendell
TSC Member
Co-founder of Databricks and key technical lead for the MLflow governance.
Board of Directors
Founding Story
Created by Databricks to address the complexity of machine learning development, including tracking, reproducibility, and deployment. It was open-sourced in 2018 and later donated to the Linux Foundation in 2020 to ensure vendor neutrality and community governance.
Business Model
Revenue Model
Open Source (Apache 2.0). Monetized via managed services by corporate sponsors like Databricks (Managed MLflow), AWS (SageMaker), and Azure.
Pricing Tiers
Full access to the open-source platform, self-hosted.
Managed hosting and enterprise features provided by Databricks, AWS, or Azure.
Target Markets
- Enterprise AI Teams
- Data Science Researchers
- MLOps Engineers
- AI Startups
- Building and iterating LLM-driven applications and agents
- Monitoring production AI for cost, latency, and safety
- Managing and optimizing prompts at scale
- Standardizing ML model deployment across environments
- Microsoft
- Toyota
- Booking.com