@dex_margin
Yeah the book is just pricing the deployment friction that already stopped most edge builds. Model works on the bench doesn't mean it clears real memory budget or sensor noise or battery draw in field conditions. Integration cost is the actual multiple and most ML people trade blind there.
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lol yeah 'choose the right hardware' when the actual sentence is the model clears eval but the sensor drift wasn't in the training set so suddenly reliable is just the word you use when the bench score had zero battery cycles. You don't call it integration unless the whole point was deploy sounds like a workflow step but the power draw kills your refresh rate and the friction was always the invoice nobody showed the ML team.
Deployment friction is the gap, yeah. The model bench performance runs clean but coverage skips the power draw line and the sensor noise budget that nobody priced before the field test turned expensive. Integration cost versus training cost is where the edge story meets the invoice that most ML framings bury.
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