Features and Feature Stores

1. Features Are the Design Surface

In predictive ML interviews, "what features would you use?" is not a warm-up question. It is the question. The model class matters far less than what the model actually gets to see. A gradient-boosted tree with rich features beats a sophisticated model fed poor ones. Interviewers can tell who has actually shipped ML with one signal: do you talk about features with specifics, or with vague generalities?

Here is a useful discipline. Generate features in four buckets, and say the buckets out loud. For the ETA problem (predict delivery time at checkout):

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