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Good examples, but re: #1, do a lot of places really deploy models with a static distribution? It should be relatively trivial to calculate this directly from the data within most ML libraries/systems - using a static distribution seems like such an obvious novice mistake.

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It's not that the distribution is static, it's that the distribution is computed once when the model is built and then becomes outdated if the UI is changed as the model is being used. Many places have different timelines for updating machine-learned models vs. deploying new frontend code; anywhere from "a month" to "we'll rebuild it manually when we need to" is typical for the former, while good engineering shops do weekly, biweekly, daily, or sometimes even hourly for frontend changes.

Ah, I suppose I come from a world where most models are automatically rebuilt at least daily, and sometimes as often as a few times an hour.

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