Flying home into Detroit this past week working on this blog post on a plane and saw for the first time, the newly connected deck of the Gordie Howe International bridge spanning the Detroit River and connecting the U.S. and Canada. The image stuck out because, in one sense, a feature store is a bridge between the clean, consistent datasets and the machine learning models that rely upon this data. But, more interesting than the bridge itself is the massive process of coordination needed to build it. This construction effort — I think — can teach us more about processes and the need for feature stores in machine learning (ML).

Think of the manufacturing materials needed as our data and the building of the bridge as the building of our ML models. There are thousands of engineers and construction workers taking materials from all over the world, pulling only the specific pieces needed for each part of the project. However, to make this project truly work at this scale, we need the warehousing and logistics to ensure that each load of concrete rebar and steel meets the standards for quality and safety needed and is available to the right people at the right time — as even a single fault can have catastrophic consequences or cause serious delays in project success. This warehouse and the associated logistics play the role of the feature store, ensuring that data is delivered consistently where and when it is needed to train and run ML models.