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.

Grab has evolved its machine learning feature store by transitioning from a traditional model to a more sophisticated feature table design, utilizing Amazon Aurora Postgres for efficient data management and retrieval. This new architecture addresses complexities in high-cardinality data and improves atomicity, ensuring consistency and reliability in ML model serving. The feature tables enhance user experience and streamline the model lifecycle, resulting in better performance of ML models.