Does it matter if it's technically overfitting or not if everyone understands what their "one specific thing" is and how to "stitch" them together to get accurate results over a some real-world problem space? (conversely, people have to recognize the limitations.) Also, for "micro-model" as a word, appreciate having neutral vocabulary to talk about a model that doesn't solve the whole problem space, but does work for some of it. As opposed to "overfit model" or "incomplete model", which seem to cast negative connotations on a concept which is potentially useful when properly applied. (Though an eventual consensus on vocabulary likely necessary as the space matures...)
Later parts of the article introduced kick-off, iteration, and prototyping time as concrete benefits. Interested to see a follow-up addressing how micro-models fit into general problem-solving pipeline. What's next in terms of speeding up the assembly-line process? Where do they fit into data-oriented programming on the whole?