Recommendation System

This is a full production recommendation system architecture with Redis feature stores, multi-tier caching, and A/B testing built in. You get FastAPI serving, cache invalidation strategies, and monitoring for CTR, conversion rate, and diversity metrics. It handles the annoying stuff like cold start fallback to popular items, thundering herd prevention with TTL jitter, and cache invalidation on user actions. The code examples are practical, showing exactly how to set up tiered caching and serve multiple models simultaneously. If you're building personalization at scale and need sub-200ms latency with proper experiment tracking, this gives you the patterns without reinventing infrastructure.