Features and feature stores

What goes into the model and how it gets there: user, item, context and cross features; encoding numbers, categories and text; batch, streaming and request-time freshness; the feature store's offline and online halves; training–serving skew and how logging served features cures it; point-in-time joins; and embeddings, their size and their versioning.