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Disclaimer: I have no experience implementing any kind of ML.

How easy will it be to update your model if/when the downstream process changes?

At a previous job we had a process that relied heavily on visual inspection from employees. I often considered applying ML to certain inspection steps, but always figured it would be most useful for e.g. final inspection to avoid having to update the models frequently as the processes were updated.

That's an interesting concern that I hadn't considered if I'm understanding you correctly. I'm imagining you could have a situation where a downstream process change helps mitigate the effect of the upstream defect. In that situation your measure of what constitutes good and bad parts will need to change in the ML model.

I think I'm somewhat lucky in that with my product downstream processes are unlikely to change in a significant enough way to warrant "retraining" the model, but I guess that's probably the only way to handle that - retrain in the event of a significant process change. Our product stays fairly stable once it releases to production and the nature of the downstream processes is that they would have very little effect on the perceived severity of the defect at the final electrical test.

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