Client-Side Caching Improves Feature Store Performance by 70% at DoorDash

To enable our platform to support hundreds of data driven models and produce billions of model predictions we build a robust ML platform, feature store and prediction engine. This was only the beginning as the feature store at the heart of the platform utilized multiple TB's of memory in large Redis clusters, which needed to be optimized for cost and fast loading times for the optimal customer experience. To improve the feature store performance we implemented a caching layer but …

client datascience feature performance