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sqweeeeeeeeeeeeeeeps t1_izt8ldx wrote

Reply to comment by digital-bolkonsky in What’s different between developing deep learning product and typical ML product? by digital-bolkonsky

Pytorch / Keras / Tensorflow for deep learning

And any basic ML library you want, scitkit leaen etc.

Deep learning is all about GPU usage and running long experiments in production. I’m confused what you even want

Is the question basically asking, what skills would someone specialized in DL have vs someone specializing in non-DL ML have?

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digital-bolkonsky OP t1_iztfgwk wrote

The question is about productization

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chengstark t1_iztu0do wrote

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.

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sqweeeeeeeeeeeeeeeps t1_iztlsd7 wrote

You’re still not asking a clear question. Using ML to build a product or a model being the product. If the model is the product, then your answer is “What’s the difference between an non-DL ML model and a DL model”.

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