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ARCQ AI's avatar

This is a great explanation of why machine learning development feels so different from traditional software engineering. I like that you framed it in terms of experimentation rather than just “training models,” because that really is the core of the work. The reminder that multiple training runs per model are the norm and not the exception also helps explain why scaling ML is so costly. It is the kind of perspective that engineers coming from a pure software background really need to hear.

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Devansh's avatar

These are some of my favorite insights from you.

Once you throw a bunch of these together, you should come on AI Made Simple for a guest post combining them. It will be super useful to everyone.

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