Submitted by digital-bolkonsky t3_zivwuc in deeplearning
chengstark t1_iztu0do wrote
Reply to comment by digital-bolkonsky in What’s different between developing deep learning product and typical ML product? by digital-bolkonsky
Sorry for being blunt, wtf is productization in this context, what does this word include? This is way too broad of a question, there are many nuances in ml/dl development, too many varibles could change based on a specific use case.
Simple models can be used just with the trained model and some API calls, this is the same between DL and ML. Non computational intensive tasks don’t even need GPUs/TPUs, most can even run on embedded hardwares. However they differ in amount of data required for training; data formats/ types also matter, typical ml algorithms work better with tabular data, but you wouldn’t use them for images. I mean what kind of garbage question is this lol. You can write a whole book on this.
If I get asked this question I’d ask back for a more concrete example, throwing out a generalized question only indicate the interviewer does not have the know how in ml/dl operations.
sqweeeeeeeeeeeeeeeps t1_izviw1x wrote
This. We have no context of what ML even entails here. It’s too broad.
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