What is MLflow?

MLflow is an open-source platform for managing the end-to-end machine learning (ML) lifecycle. It provides a suite of tools to help data scientists and ML engineers track experiments, package code into reproducible runs, manage and deploy models, and collaborate efficiently.

MLflow is framework-agnostic and can be integrated with any ML library, language, or existing codebase. Its modular design allows users to adopt one or more of its components as needed:

• Tracking: Log and query experiments, code, data, and results.
• Projects: Package data science code in a reusable and reproducible format.
• Models: Manage and deploy models from various ML libraries.
• Model Registry: Store, annotate, and manage model versions in a central repository.

What does MLflow offer?

• Experiment tracking and reproducibility
• Centralized model registry and lifecycle management
• Flexible deployment options for serving models
• Integration with popular ML frameworks and tools

Learn more

• Register Model in MLflow: Step-by-step guide to registering your models in MLflow for versioning and deployment.
• Enable MLflow Autolog: How to automatically log metrics, parameters, and models with MLflow autolog.

Explore the sections above to get started with MLflow and streamline your ML workflows.