Ship high-quality AI, fast

Traditional software and ML tests aren't built for GenAI's free-form language, making it difficult for teams to measure and improve quality.

MLflow combines metrics that reliably measure GenAI quality with trace observability so you can measure, improve, and monitor quality, cost, and latency.

pip install mlflow

CORE FEATURES

Observability to debug and monitor

Debug with tracing

Debug and iterate on GenAI applications using MLflow's tracing, which captures your app's entire execution, including prompts, retrievals, tool calls.

MLflow's open-source, OpenTelemetry-compatible tracing SDK helps avoid vendor lock-in.

Monitor in production

Maintain production quality with continuous monitoring of quality, latency, and cost. Gain real-time visibility via MLflow's dashboards and trace explorers.

Configure automated online evaluations with alerts to quickly address issues.

CORE FEATURES

Evaluation to measure and improve quality

MLflow simplifies GenAI evaluation, enabling easy collection and recording of LLM judge and human feedback directly on traces.

Accurately measure free-form language with LLM judges

Utilize LLM-as-a-judge metrics, mimicking human expertise, to assess and enhance GenAI quality. Access pre-built judges for common metrics like hallucination or relevance, or develop custom judges tailored to your business needs and expert insights.

Use production traffic to drive offline improvements

Adapt to user behavior by creating evaluation datasets and regression tests from production logs. Replay these to assess new prompts or app versions in development, ensuring optimal variants reach production.

Use human feedback to improve quality

Collect expert feedback through web UIs and end-user ratings from your app via APIs. Use this feedback to understand how your app should behave and align your custom LLM-judge metrics with expert judgement.

CORE FEATURES

Lifecycle management to track and version

Prompt registry

Version, compare, iterate on, and discover prompt templates directly through the MLflow UI. Reuse prompts across multiple versions of your agent or application code, and view rich lineage identifying which versions are using each prompt.

Evaluate and monitor prompt quality and performance across multiple versions.

Agent and application versioning

Version your agents, capturing their associated code, parameters, and evalation metrics for each iteration. MLflow's centralized management of agents complements Git, providing full lifecycle capabilities for all your generative AI assets.

Evaluation and observability data are linked to specific agent/application versions, offering end-to-end versioning and lineage for your entire GenAI application.

WHY US?

Why MLflow is unique

Unified, End-to-End MLOps and AI Observability

MLflow offers a unified platform for the entire GenAI and ML model lifecycle, simplifying the experience and boosting collaboration by reducing tool integration friction.

Open, Flexible, and Extensible

Open-source and extensible, MLflow prevents vendor lock-in by integrating with the GenAI/ML ecosystem and using open protocols for data ownership, adapting to your existing and future stacks.

Enterprise-Grade Security & Governance on a Unified Data & AI Platform

Managed MLflow on Databricks offers enterprise-grade security and deep Mosaic AI integrations for enhanced datasets, development, RAG, serving, and gateways. Unity Catalog ensures centralized governance over all AI assets.

Unlock Downstream Value with Databricks AI/BI

Leverage your GenAI and ML data for downstream business processes by building rich performance dashboards, reports, and queries with Databricks AI/BI and Databricks SQL.