How do you design feature stores for large-scale time series systems?

A feature store centralizes feature computation, storage, and serving for consistent and reusable time series features.

Feature stores manage time series features like lags, rolling statistics, and external signals in a centralized system. They ensure consistency between training and inference by eliminating feature drift. They also support point-in-time correctness, preventing leakage by ensuring only past data is used for feature computation.