MLOps Platforms at Scale
How Uber and Netflix democratized machine learning across thousands of engineers
Why These Case Studies Matter
The difference between a ML model in a notebook and a model in production is often 6-12 months of engineering work. Uber and Netflix built platforms that reduced this to days, enabling thousands of data scientists to deploy models independently.
These case studies reveal the complete MLOps stack: feature stores, experiment tracking, model serving, monitoring, and A/B testing. You'll learn architectural patterns that enable rapid experimentation while maintaining production reliability at massive scale.
Learning Path: After reading these case studies, build your own MLOps pipeline with the PredictFlow MLOps Project, then follow the step-by-step walkthrough.
Note on Metrics: These case studies are based on publicly available information from engineering blogs, conference talks, and open-source documentation. While we've verified core architectural patterns and technologies, some specific numbers (especially cost figures and exact scale metrics) are estimates for educational purposes. Where possible, we've updated unverified claims to reflect documented information or general ranges.
Featured Case Studies
Deep dives into Uber Michelangelo and Netflix's ML platforms
Uber - Michelangelo Platform
Case Study #1
The Problem
Data scientists struggled to deploy ML models to production. Manual deployment took weeks, no standardization across teams, and no visibility into model performance. Needed platform to democratize ML across 10,000+ engineers.
Scale
Netflix
Case Study #2
The Problem
Recommendation models drove 80% of viewing but took months to develop and deploy. Manual ML workflows didn't scale to hundreds of data scientists. Needed end-to-end platform from experimentation to production serving at 200M+ users.
Scale
Continue Learning
Build Your Own MLOps Pipeline
Practice with the PredictFlow project - implement experiment tracking and model serving
Troubleshooting Guide
Common MLOps errors - from train/serve skew to model drift
Step-by-Step Walkthrough
Complete walkthrough for building the PredictFlow pipeline from scratch
More Case Studies
Explore how companies use Spark, LLM pipelines, RAG, and other technologies