Major Streaming Platform

A major streaming platform's ad revenue depended on real-time bidding auctions completing in under 100ms. Their existing ML models for bid prediction were accurate but slow, causing lost auctions and millions in unrealized revenue. The MLOps pipeline couldn't keep up with model retraining demands as viewer behavior shifted.

Redesigned the ML inference pipeline for sub-100ms prediction: 1. Model distillation to compress large ensemble models into lightweight real-time inference models. 2. Built a feature store for pre-computed viewer profiles, reducing per-request computation. 3. Implemented A/B testing framework for continuous model improvement. 4. Kafka-based event streaming for real-time feature updates. 5. Kubernetes-based auto-scaling to handle peak traffic — NFL games, series premieres.

Platform processes millions of bid requests daily across 5M+ devices. Inference latency reduced to <50ms. Ad revenue increased through higher win rates and better targeting. MLOps pipeline enables weekly model retraining with automated validation and rollback.