Machine Learning Systems at Scale

This program bridges the gap between academic ML research and industrial-scale deployment. You will learn MLOps, distributed training, model serving, A/B testing, and the full ML lifecycle in production environments. Case studies from Google, Meta, Netflix, and Uber provide real-world context. You will build end-to-end ML pipelines, implement feature stores, and design monitoring systems for production models.