
Ask HN: Why companies are not using deep learning yet? - avin_regmi
I&#x27;ve noticed most companies are using traditional machine learning such as SVM, Random Forrest..etc in production. Also, most are using PySpark ML rather than deep learning frameworks such as tensorflow and pytorch. Why people are not using deep learning in production yet? What framework are you guys using in production?
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avin_regmi
Inferring the model with Flask is slow and requires custom code for caching
and batching. Scaling in multiple machines using Flask also causes many
complications. To address these issues, we have developed Panini.
[https://www.panini.ai](https://www.panini.ai) What do you guys think?

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muzani
It's not always the best solution for everything. Sometimes a dumb algorithm
does the same thing more effectively.

Deep learning requires _lots_ of data, and at best, it's about as effective as
a dumb foreign worker. It's not going to replace any jobs soon and it's a very
long game.

Also if you train it on garbage data, you get garbage results. Not everyone
has access to clean data. When many people say data is the new oil, really
they're just putting a mountain into a blender and expecting deep learning to
find the oil.

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natalyarostova
To elaborate on your point, it doesn't just require lots of data, it requires
lots of data with a corresponding high effective sample size. If you want to
forecast sales for next Christmas, I don't care if you have 2000 terabytes of
granular orders and sales data, because the effective sample size for past
observed Christmases is going to be like 3-4 (Christmases further than 4 years
back may no longer be representative).

In these cases nice structural time-series models, which are in spirit not so
different from what existed 20 years ago, will beat deep learning.

~~~
avin_regmi
What frameworks are you using currently in production? Do you think classical
machine learning algorithms are being used more than deep learning?

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schappim
They are not using deep learning because frameworks such as tensor flow are
developer tools and (until everyone can code) most work in a company is still
done by non-developers.

This is why Uber’s Ludwig[1] is so interesting. With a tool like this I can
have non-dev staff creating solutions (the same way that they can create
solutions using a spreadsheet).

1\.
[https://uber.github.io/ludwig/examples/](https://uber.github.io/ludwig/examples/)

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avin_regmi
I just checked Uber's Ludwig. It looks very cool and super easy to use. Are
companies using this in production? How will I serve the model once I train it
on Ludwid?

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schappim
Uber purport to be doing so...

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laurentl
Another point to note is that production code needs to meet a higher bar in
terms of testing and auditability. It’s one thing to train a NN on a data
scientist’s machine and show that on test data, the new system performs 10%
better; and another to put what is essentially a black box in production and
use that black box to make financial or business decisions which can have far-
reaching consequences _on data it’s never seen and which is potentially
outside of the scope of what it was trained on_.

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avin_regmi
Do you know which frameworks, companies are using in production?

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rayvy
A few: [https://www.marutitech.com/top-8-deep-learning-
frameworks/](https://www.marutitech.com/top-8-deep-learning-frameworks/)

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tmaly
In the financial industry, there are rules that require you to explain how the
algorithm works. That is hard to due with neural networks.

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verdverm
Because deep learning is expensive compared to simpler methods which are
sufficient for business needs.

i.e. the newest or shiniest thing is not always the best choice for the
business. Or it's not about the tech, it's about the value creation

For more examples, see blockchain

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pizza
Curious, what about deep learning is the expensive part? The developer time-
cost for implementations? Time spent creating the model, or data collection +
cleaning, or (owning/renting) the hardware? Because I have a sense that these
have become a lot cheaper in the last few years (ok, maybe not the developer
time-cost)

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avin_regmi
Yes, I think it has been lot cheaper and and more easier to train state of the
art models such as fast ai.

