This article details how Dropbox created a custom feature store to enhance the search and ranking system in Dropbox Dash. It discusses the challenges of integrating on-premises and cloud systems, achieving low latency for feature retrieval, and ensuring data freshness in response to user behavior.

The article discusses the transformation of a batch machine learning inference system into a real-time system to handle explosive user growth, achieving a 5.8x reduction in latency and maintaining over 99.9% reliability. Key optimizations included migrating to Redis for faster data access, compiling models to native C binaries, and implementing gRPC for improved data transmission. These changes enabled the system to serve millions of predictions quickly while capturing significant revenue that would have otherwise been lost.