This blog post discusses the evolution of Grab's machine learning feature store, detailing the transition from a traditional model to a more sophisticated system leveraging Amazon Aurora Postgres. It highlights the challenges faced due to increased complexity in ML systems, the introduction of feature tables for better management, and the rationale behind using Aurora including its cost-effectiveness and performance. It further explores the benefits of decentralised deployments, enhancing the scalability and performance of their ML infrastructure.