Michelangelo (Uber’s ML Platform): Going Open Source | Sally Lee & Eric Wang, Uber

In this talk at MLOps World 2025, Sally Lee and Eric Wang from Uber present Michelangelo, Uber’s end-to-end machine learning platform, and announce its upcoming open-source release. They describe the platform’s evolution over the past decade, from classical ML to deep learning and now to a multi-agent platform. Michelangelo supports over 25,000 model trainings per month and serves 6,000 models in production, handling 30 million predictions per second. The platform is built on Uber’s infrastructure and supports multi-cloud deployments. The talk includes a live demo by Eric Wang, illustrating the data scientist and ML engineer journeys. Key features highlighted include project scaffolding, a Python-first workflow engine called Uniflow, model registry, evaluation pipelines, retraining processes, and safe deployment strategies like Jono rollout. The speakers emphasize the platform’s plug-in architecture, which allows integration with external systems. They announce that the first components, including Uniflow and the control plane, will be open-sourced later this year, with the full platform expected to be public early next year. They are seeking partnerships for early adoption and collaboration.