TLDR Tech / Data
Building a High-Scale Real-Time Recommendation Engine with Feature Stores and Redis Observability
Real-time recommendation systems now need to combine rich contextual features with sub-100 ms latency at scale, often across billions of interaction records. Feature stores act as the consistency layer between offline training and online serving, reducing training-serving skew, while batch platforms compute expensive features and embeddings. Redis is used for low-latency vector similarity search, candidate retrieval, and caching eligibility filters, keeping request paths fast and efficient.
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