MLflow
Originally by Databricks
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, reproducible packaging of code, a model registry, and deployment tooling.
Definition
MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experiment tracking, reproducible packaging of code, a model registry, and deployment tooling.
Overview
MLflow is organized around four main components: MLflow Tracking, which logs parameters, metrics, and artifacts from training runs; MLflow Projects, which packages ML code in a reusable, reproducible format; MLflow Models, a standard format for packaging models so they can be deployed across different serving tools; and the Model Registry, which manages model versions and their stage in the lifecycle (staging, production, archived). Because it is open source and framework-agnostic, MLflow works with PyTorch, TensorFlow, scikit-learn, and many other libraries, and can be self-hosted or run as a managed service, notably within Databricks, the company that originally created it. MLflow is one of the most widely adopted tools in the MLOps space, often compared with hosted platforms like Weights & Biases and Comet ML; its open-source, self-hostable nature makes it a common choice for teams that want to avoid vendor lock-in or need full control over their ML infrastructure.
Key Features
- Experiment tracking for parameters, metrics, and artifacts
- Reproducible project packaging for ML code
- Standardized model packaging format for cross-tool deployment
- Model registry for versioning and lifecycle management
- Framework-agnostic support for PyTorch, TensorFlow, and more
- Open source with options for self-hosting or managed deployment
- Integration with Databricks and other cloud ML platforms