
Ask HN: How do you train and deploy machine learning models at your company? - geoffxy
Hey HN!<p>I’ve been reading a lot about ML and I’ve been curious: how do you train and serve models (for example, deep neural networks) at your company? Do you use a custom system or something else? How do you deploy&#x2F;serve your models in production? Are there any particular pain points with your current approach?<p>Thanks!
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GFischer
Curious as well. Have been looking at stuff like
[https://www.floydhub.com/](https://www.floydhub.com/) and
[https://www.datarobot.com/product/](https://www.datarobot.com/product/) \- we
have some use cases we want to implement but nothing built yet.

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uptownfunk
Flask/Docker/Postgres.. bit of a learning curve but more than enough resources
online to get going.

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eggie5
so you save your model weights to Postgres, expose the model via an HTTP api
using flask and package it w/ docker?

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uptownfunk
Full disclosure: I am a data scientist not a production-grade SWE.. in any
case that sounds about right. I would probably just leave the weights loaded
in memory which might be lower latency than querying a db every time you want
the score. The DB is mainly there to store the json requests (or however
you're receiving them) as well as the predictions for future use.

Another use case for the db would be if someone sends you data to score and
you have to append other external data tables to that data, in which case you
could use the db to append the data.

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Jack000
not really a company, but for my (profitable) side projects I just serve the
models with flask/wgsi, on a single server.

its much cheaper than the cloud solutions, but I suspect it will fall over if
it ever gets on hn..

