mlflow/mlflow

The open source AI engineering platform for agents, LLMs, and ML models. MLflow enables teams of all sizes to debug, evaluate, monitor, and optimize production-quality AI applications while controlling costs and managing access to models and data.

What it solves

MLflow is an AI engineering platform designed to help teams build, debug, evaluate, and deploy production-quality AI applications, including LLM-based agents and traditional machine learning models. It addresses the complexity of managing the full AI lifecycle, from initial experimentation to production monitoring and cost control.

How it works

MLflow provides a suite of integrated tools that connect to AI workflows:

  • Observability: Uses OpenTelemetry to capture complete traces of LLM applications and agents, providing behavioral insights and monitoring for quality, cost, and safety.
  • Evaluation: Offers over 50 built-in metrics and LLM judges to systematically track quality and catch regressions.
  • Prompt Management: Allows for the versioning, testing, and deployment of prompts with full lineage tracking and automated optimization algorithms.
  • AI Gateway: Acts as a unified API gateway for LLM providers to manage rate limits, fallbacks, credentials, and traffic splitting for A/B testing.
  • ML Lifecycle Tools: Includes experiment tracking for parameters and metrics, a model registry for collaborative management, and deployment tools for various environments like Kubernetes and AWS SageMaker.

Who it’s for

AI engineers and data science teams of all sizes who are developing LLM applications, AI agents, or traditional ML models and need a production-grade platform to manage their development lifecycle.

Highlights

  • Broad Integration: Supports Python, TypeScript/JavaScript, and Java, with one-line automatic tracing for over 60 frameworks.
  • Comprehensive LLMOps: Combines tracing, evaluation, prompt optimization, and governance in one platform.
  • Scalable Infrastructure: Supports deployment to Docker, Kubernetes, Azure ML, and AWS SageMaker.
  • Open Standard: Natively integrates with OpenTelemetry and MCP.