Part 3 of 5 in the series: When Your AI Pipeline Grows Up
In the previous post, we worked through the pipeline architecture that gets features from raw events to a computed state. Now we need to talk about where those features live once they're computed — and how they get from storage to your model at inference time.
That's the feature store's job.
The feature store is the operational center of a real-time ML system. It sits between the pipeline that produces features and the model that consumes them. Get it right, and you have a foundation for every model you'll build. Get it wrong, and you'll spend years firefighting problems that trace back to a design decision made early on.
The central tension in feature store design is this: you need consistency and low latency simultaneously, at scale. Those goals pull in different directions. Understanding why — and what architectural patterns resolve the tension — is what this post is about.
