How Uber's Michelangelo ML platform evolved from predictive models to generative AI at scale
Before Michelangelo, Uber's ML development was fragmented: applied scientists used Jupyter Notebooks and engineers built bespoke deployment pipelines with no system for reliable reproducible workflows, no easy way to compare training experiments, and no established path to production deployment.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Unified ML project initiation
MA Studio provides a simplified user flow covering every step of the ML journey from feature/data prep through production performance monitoring.
Michelangelo now manages approximately 400 active ML projects with over 20K model training jobs monthly, more than 5K models in production serving 10 million real-time predictions per second at peak, and deep learning adoption in tier-1 projects increased from almost zero to more than 60%.
What failed first
Michelangelo 1.0 had four structural gaps: no comprehensive ML quality definition or project tiering, insufficient deep learning support, inadequate collaborative model development capabilities, and fragmented ML tooling forcing developers to constantly switch between semi-isolated systems.