MLflow 3 Launch: Redefining MLOps for the GenAI Era

[Yuki Watanabe]

MLflow 3 is now available to everyone, marking the biggest evolution in the best open-source MLOps platform's history and transforming how millions of developers build, deploy, AI applications. While previous versions focused on traditional ML workflows, MLflow 3 fundamentally reimagines the platform for the GenAI era. This isn't just an update, but a complete paradigm shift that brings production-grade GenAI capabilities to the open source community for the first time.

🎯 Improved Model Tracking for GenAI
MLflow 3 introduces versioning mechanism purpose-built for GenAI applications not only model artifacts. The new LoggedModel entity serves as a metadata hub, linking each conceptual application version to its specific external code (e.g., a Git commit), configurations, with other MLflow entities like traces and evaluation runs. The new versioning mechanism also work seamlessly for traditional ML models and deep learning checkpoints..

🔗 Comprehensive Performance Tracking & Observability
Enhanced model tracking provides comprehensive lineage between models, runs, traces, prompts, and evaluation metrics. The new model-centric design allows you to group traces and metrics from different development environments and production, enabling rich comparisons across model versions.

📊 Production-Grade GenAI Evaluation
MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle. From development through production, use the same quality scorers to ensure your applications deliver accurate, reliable responses while managing cost and latency. Visit documentation for more details.

👥 Human-in-the-Loop Feedback
Real-world GenAI applications need human oversight. MLflow 3 now tracks human annotations and feedback for model predictions, enabling streamlined human-in-the-loop evaluation cycles. This creates a collaborative environment where data scientists, domain experts, and stakeholders can efficiently improve model quality together.
(Note: Currently available in Databricks Managed MLflow. Open source release coming in the next few months.)

⚡️ State-of-the-Art Prompt Optimization
Transform prompt engineering from art to science. The MLflow Prompt Registry now includes prompt optimization capabilities built on top of the state-of-the-art research, allowing you to automatically improve prompts using evaluation feedback and labeled datasets. This includes versioning, tracking, and systematic prompt engineering workflows.

📚 Revamped Website and Documentation
The MLflow documentation and website has been fully redesigned to support two main user journeys: GenAI development and classic machine learning workflows. The new structure offers dedicated sections for GenAI features (including LLMs, prompt engineering, and tracing), and traditional ML capabilities such as experiment tracking, model registry, deployment, and evaluation.

▶︎▶︎▶︎ Ready to Get Started?　▶︎▶︎▶︎
Get up and running with MLflow 3 in minutes: pip install 'mlflow>=3.1'

Resources:
🌐 New Website | 📖 Documentation | 🎉 Release Notes

🏎️ The Road Ahead 🏎️
It is just the beginning. The open source community continues driving innovation toward the world's best open-source MLOps/LLMOps platform. Here's how you can be part of the journey:

How to Get Involved:
🔧 Contribute Code: From bug fixes to major features, all contributions welcome
🐝 Report Issues: Help us improve by reporting bugs and requesting features
💬 Join Discussions: Technical discussions, roadmap planning, and peer support
📝 Share Your Story: Write blogs, tutorials, and docs about your MLflow implementations to help others learn!

The future of AI development is unified, observable, and reliable. MLflow 3.0 brings that future to the open source community today.

Ready to transform your GenAI workflow? Get started now→