Building a Scalable ML Feature Store with Redis

When a company with millions of consumers such as DoorDash builds machine learning (ML) models, the amount of feature data can grow to billions of records with millions actively retrieved during model inference under low latency constraints. These challenges warrant a deeper look into selection and design of a feature store -- the system responsible for storing and serving feature data. The decisions made here can prevent overrunning cost budgets, compromising runtime performance during model inference, and curbing model deployment velocity. Features are the input variables fed to an ML model for inference. A feature store, simply put, is a key-value store that makes this feature data available to models in production. At DoorDash, our existing feature store was built on top of Redis, but had a lot of inefficiencies and came close to running out of capacity. We ran a full-fledged benchmark evaluation on five different key-value stores to compare their cost and performance metrics.