Uber Migrates Michelangelo Platform To Kubernetes Foundation
Uber reengineered its Michelangelo ML platform in 2026, moving from a monolithic stack to a cloud-native Kubernetes foundation to overcome scaling limits. Engineers introduced 100+ CRDs with transparent MySQL-backed persistence, a federation layer achieving 99.9% scheduling success, Python-native Uniflow workflows, and a multi-cloud compute mesh. The platform now supports over 30 million predictions per second and 40 million daily trips, demonstrating large-scale MLOps patterns.
Key Points
- 1Define 100+ purpose-built CRDs to represent ML lifecycle and enable Kubernetes-native control
- 2Synchronize metadata to scalable MySQL backend to bypass etcd limits, enabling millisecond joins and queries
- 3Implement federation and Virtual Regional Clusters for 99.9% scheduling success and efficient capacity utilization
Scoring Rationale
High operational scale and actionable platform patterns, offering credible industry lessons but limited academic novelty beyond implementation specifics.
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