Build with MLflow GenAI
Learn how to get started with MLflow for building production-ready GenAI applications.
Get Started
Build your first GenAI app with MLflow
Environment Setup
Install and configure MLflow
API Reference
Complete API documentation
Tracing & Debug
Observe AI application flows
Evaluation
Test and improve AI quality
Prompt Engineering
Design and version prompts
Deploy & Serve
Production deployment guide
Classical ML
Traditional ML workflows
Integrations
Framework connections
Why choose MLflow?
Open Source
Join thousands of teams building GenAI with MLflow. As part of the Linux Foundation, MLflow ensures your AI infrastructure remains open and vendor-neutral.
Production-Ready Platform
Deploy anywhere with confidence. From local servers to cloud platforms, MLflow handles the complexity of GenAI deployment, monitoring, and optimization.
End-to-End Lifecycle
Manage the complete GenAI journey from experimentation to production. Track prompts, evaluate quality, deploy models, and monitor performance in one platform.
Framework Integration
Use any GenAI framework or model provider. With 20+ native integrations and extensible APIs, MLflow adapts to your tech stack, not the other way around.
Complete Observability
See inside every AI decision with comprehensive tracing that captures prompts, retrievals, tool calls, and model responses. Debug complex workflows with confidence.
Automated Quality Assurance
Stop manual testing with LLM judges and custom metrics. Systematically evaluate every change to ensure consistent improvements in your AI applications.
Running Anywhere
MLflow can be used in a variety of environments, including your local environment, on-premises clusters, cloud platforms, and managed services. Being an open-source platform, MLflow is vendor-neutral; no matter where you are doing machine learning, you have access to the MLflow's core capabilities sets such as tracking, evaluation, observability, and more.
