Draft:MLflow
Open source AI software project
From Wikipedia, the free encyclopedia
MLflow is an open source platform for machine learning, LLM applications, and AI agents. Originally developed for machine learning lifecycle management, it provides experiment tracking, a model packaging format, a model registry, and model deployment functionality. Later releases expanded the platform to support LLM applications and AI agents, adding tools for observability, evaluation, prompt management, and LLM access control. Originally created by Databricks and first released in June 2018,[1] MLflow is licensed under the Apache License 2.0 and is a Linux Foundation project.[2][3]
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| MLflow | |
|---|---|
| Initial release | June 5, 2018 |
| Written in | Python, JavaScript, TypeScript, Java, R |
| Type | AI engineering platform, LLMOps, MLOps |
| License | Apache License 2.0 |
| Website | mlflow |
| Repository | github |
Capabilities
Machine learning
For machine learning and deep learning workflows, MLflow covers the development lifecycle from experimentation to deployment. It provides experiment tracking for logging and comparing model parameters, metrics, and files across training runs. Models can be packaged in a standardized format compatible with frameworks including scikit-learn, PyTorch, TensorFlow, and Spark ML, and managed through a model registry.[4][5] MLflow also supports hyperparameter optimization and model serving via REST APIs.
AI agents and LLMs
MLflow 3.0, released in June 2025, added support for building and deploying AI agents and LLM applications.[6][7] According to project documentation, the release introduced tools for capturing execution traces compatible with the OpenTelemetry standard, an evaluation framework measuring response quality using LLM-as-a-judge scoring and human feedback, versioned storage for prompt templates, and an AI gateway for routing requests and controlling access to LLM providers such as OpenAI, Anthropic, and Google Gemini.[8][9]
History
| Version | Date | Notes |
|---|---|---|
| 0.1.0 | June 2018 | Initial release, announced at the Databricks Spark+AI Summit.[10][11][12] |
| 1.0 | June 2019 | Added loss curve tracking, performance improvements, and Windows support.[13][14][15] The Model Registry was introduced shortly thereafter.[16] |
| 2.0 | November 2022 | Introduced a model evaluation SDK, overhauled UI, and pre-built ML training pipelines.[17][18] |
| 3.0 | June 2025 | Added tracing, evaluation, prompt management, and gateway features for AI agents and LLM applications.[19][6] |
Adoption
MLflow is offered or supported by several vendors and cloud platforms. Databricks, the company that originally created MLflow, offers it as a managed service within its data and AI platform.[20] Amazon Web Services integrated MLflow within Amazon SageMaker,[21] and Azure Machine Learning supports MLflow tracking and model deployment natively.[22] Canonical released Charmed MLflow, an enterprise distribution for Ubuntu.[23] InfoWorld included MLflow in its annual Best Open Source Software awards in 2019[24] and 2021.[25]