MLflow

Overview

MLflow is an open-source platform for managing the complete ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. Created by Databricks and donated to the Linux Foundation, MLflow has become one of the most widely adopted MLOps tools with a large open-source community.

The platform is framework-agnostic and works with any ML library, making it versatile for teams using different tools. MLflow can be self-hosted for free or used as part of Databricks' managed offering, providing deployment flexibility.

Key Features

**Experiment Tracking**: Log parameters, metrics, artifacts

**Model Registry**: Centralized model versioning

**Projects**: Package reproducible ML code

**Models**: Deploy to various platforms

**Framework Agnostic**: Works with any ML library

**Self-Hosted**: Full control over infrastructure

**REST API**: Programmatic access

**UI**: Web-based visualization

When to Use MLflow

MLflow is ideal for:

Teams wanting open-source MLOps

Organizations requiring self-hosted solutions

Databricks users (integrated experience)

Framework-agnostic ML workflows

Companies avoiding vendor lock-in

Academic and research projects

Pros

Fully open-source and free

Large community and ecosystem

Framework-agnostic design

Self-hosting option

Good Databricks integration

Active development

No vendor lock-in

Flexible deployment

Cons

Requires self-hosting and management

Less polished UI than W&B

Basic features compared to commercial tools

Setup can be complex

Limited collaboration features

Scaling requires effort

Less intuitive than alternatives

Documentation could be better

Pricing

**Open Source**: Free, Apache 2.0 license

**Self-Hosted**: Free to deploy anywhere

**Databricks**: Included in Databricks platform