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Blasket_Basket t1_izv8icg wrote

I see a lot of people mentioning needing a GPU for DL, but it appears no one has yet clarified you only need that for training.

If you're looking for the standard use case of training a model, saving it, and then productionizing that model by exposing an API for model inference only, then you only need a GPU for the training phase. For inference, you do not need a GPU. AWS rents specialized EC2 instances with fast CPUs optimized specifically for model inference.

Another major difference may be that business requirements may preclude the use of Deep Learning in the solution. For instance, business areas like credit risk are regulated and require a level of model explainability that we can't provide with neural networks.

Others have already made great comments regarding tabular vs unstructured data, no other comments to add there.

One final thing area is the sheer volume of data needed for a DL solution vs a "Shallow" ML solution. You need orders of magnitude more data to successfully train most DL models than you do to get good performance with most other ML algorithms.

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tech_ml_an_co t1_izt4bd0 wrote

Quite different tech stack for APIs. DL requires some kind of model server with GPU. For traditional ML use Lambda or FastAPI on a server.

For batch processing it's more similar, depending on your data size, you might not need a GPU even for Deep learning.

Also deep learning is usually unstructured data, which requires different storage and training infrastructure.

You can read books about that topic however at the core that's the difference a that's why a lot of companies still don't utilize DL.

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digital-bolkonsky OP t1_izt4z79 wrote

Right so when it comes to computing if I am building a DL api for someone. How should I address the computing issue?

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sqweeeeeeeeeeeeeeeps t1_izspjid wrote

Google difference between ML and Deep Learning.

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digital-bolkonsky OP t1_izt53ya wrote

The question is about development and tech stack

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sqweeeeeeeeeeeeeeeps t1_izt8ldx wrote

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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suflaj t1_izswui5 wrote

From the top of my head, DL requires much more data preprocessing and research. ML is more like - fit an XGBoost model, and if it doesn't work well, see why, fix that in data and try again. If XGBoost can't solve it, your data is bad or you need DL.

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