MLflow
Founded
2018Products
1 product listedAvg. AI Score
8.5/10Website
mlflow.org/
Profile updated
About MLflow
MLflow is an open-source platform designed to manage the end-to-end machine learning lifecycle. It was created by Databricks and first released in June 2018. The project is hosted under the Linux Foundation's AI & Data umbrella and has grown into one of the most widely adopted open-source ML lifecycle tools available.
MLflow provides four core components: Tracking, for logging experiments and comparing parameters and metrics; Projects, for packaging reproducible ML code; Models, for packaging and deploying models across serving environments; and Model Registry, for managing model versioning and stage transitions. The platform supports frameworks including TensorFlow, PyTorch, scikit-learn, and XGBoost. More recently, MLflow has expanded to support LLM evaluation, tracing for AI agents, and prompt management. It is used by data science and ML engineering teams across industries ranging from finance to healthcare.
MLflow is an open-source project and does not operate as an independent commercial entity with its own funding or headcount. Databricks, its primary corporate sponsor, raised $500 million at a $43 billion valuation in 2023. The MLflow GitHub repository has accumulated over 18,000 stars and contributions from hundreds of external contributors, reflecting broad community adoption beyond Databricks itself.
Specialties
Products by MLflow
All products by MLflow reviewed by our AI panel of experts.
How Our Review Panel Scores MLflow
Six independent reviewer personalities assessed MLflow's product on their own terms. They landed within 0.6 points of each other, an unusually strong consensus.
Averaged across 1 reviewed product in our catalog. Scores are out of 10.
Competitive Landscape
Who MLflow competes with, and how they differ.
Proprietary, managed experiment-tracking platform sold per seat, strongest on collaborative dashboards and hyperparameter sweeps. MLflow covers the same tracking ground as Apache-2.0 open source that teams self-host or consume via Databricks, and adds model packaging and a registry.
1 product reviewed on TopReviewed
Open-source MLOps suite that bundles experiment tracking with built-in job orchestration, remote execution, and data management. MLflow stays a lighter-weight lifecycle framework and leaves scheduling and compute orchestration to external tools.
1 product reviewed on TopReviewedKubernetes-native open-source ML toolkit centered on pipeline orchestration across a cluster. It is heavier to deploy and operate than MLflow, which runs anywhere Python does and focuses on tracking, packaging, and the model registry rather than workflow orchestration.
Commercial experiment-tracking and model-monitoring platform delivered as managed SaaS, with the open-source Opik tool for LLM evaluation. It competes with MLflow on tracking and GenAI observability but monetizes hosting, while MLflow is a fully open-source framework.
1 product reviewed on TopReviewedCompany Timeline
Key milestones in MLflow's history, with sources.
- Launch
MLflow 3.0 released with GenAI capabilities
The 3.0 release introduced the LoggedModel entity, GenAI tracing with lineage across models, runs, prompts, and evaluation metrics, an LLM-judge evaluation suite, and prompt optimization in the Prompt Registry.
mlflow.org - Launch
MLflow 2.0 released
The 2.0 release added MLflow Recipes with AutoML, hyperparameter tuning, and classification support, plus a revamped experiment-tracking UI and a stable evaluate API. Monthly downloads had surpassed 13 million.
databricks.com - Milestone
MLflow joins the Linux Foundation
Databricks donated MLflow to the Linux Foundation to give it a vendor-neutral home with open governance. At donation the project had over 200 contributors and more than 2 million downloads per month, growing 4x annually.
linuxfoundation.org - Launch
MLflow 1.0 released
The 1.0 release marked the stability of the core APIs. The PyPI package was being downloaded close to 600,000 times a month, with more than 100 contributors and deployments at thousands of organizations.
databricks.com - Partnership
Microsoft joins the MLflow project
Microsoft became an active contributor and added native MLflow tracking support to Azure Machine Learning. Within its first year the project counted over 500,000 monthly downloads, 80-plus contributors, and 40 contributing organizations.
databricks.com - Founded
MLflow announced and open-sourced at Spark + AI Summit
Databricks unveiled MLflow as an open-source machine learning platform with three components - Tracking, Projects, and Models - during a keynote by co-founder Matei Zaharia in San Francisco.
databricks.com
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