
Moving machine learning from practice to production - ramanan
https://engineering.semantics3.com/2016/11/13/machine-learning-practice-to-production/
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ramanan
OP here, the post was really meant to invite discussion.

I would love to hear more from people deploying machine learning techniques at
their projects, teams and companies.

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thomaso
We touched on a lot of these questions in this talk about how we went from a
prototype to a production machine learning system:
[https://vimeo.com/181931334](https://vimeo.com/181931334)

The main point I haven't seen mentioned that often is to constantly verify
your data and your data processing pipeline. We treat these checks as
integration tests and run them as part of our continuous integration system.
We also use New Relic to monitor model freshness, to be alerted if any part of
the pipeline has broken.

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iliketosleep
years ago i dealt with NN's in automated trading, and little regard was given
to source data requirements. it was like NN's were treated as magic boxes
rather than black boxes. a magic box will miraculously produce useful output
even when you feed it garbage, but a black box has specific requirements for
its data source to make it useful. so i like how the author dedicates one
entire part of the three-part flow chart to sources and preprocessing. if i
deal with NN's again i'll definitely place more emphasis on this aspect.

