MLflow AI Lifecycle Platform

MLflow is an open source platform for managing the end to end machine learning lifecycle, designed to help teams debug, evaluate, and monitor LLM applications, AI agents, and traditional models, enabling ten times faster iteration on AI products. Originally developed at Databricks and donated to the Linux Foundation in 2020, the project has accumulated over 26,000 GitHub stars and become the industry standard for ML operations. Core components include MLflow Tracking for logging parameters, metrics, artifacts, and models during experiment runs with support for local file storage, SQL databases, and cloud object stores; MLflow Projects for packaging reproducible training code with conda, virtualenv, and Docker environments; MLflow Models for packaging models with a standard format that supports over 30 deployment targets including Kubernetes, SageMaker, Azure ML, Databricks, and local REST servers; and MLflow Model Registry for versioning, staging, and governance of production models with approval workflows and access controls. Additional components include MLflow Recipes and Pipelines for opinionated high quality training templates, MLflow Evaluate for automated model and LLM evaluation with standard and custom metrics, MLflow Tracing for distributed tracing of LLM application execution flows, MLflow Gateway for unified API routing to multiple LLM providers, and MLflow Prompt Engineering for interactive prompt development with deployment capabilities. The Apache 2.0 licensed platform supports Python, Java, R, and REST APIs.