Hi Andrew, I have recently started a six month AI/Machine Learning Engineering course which focuses exactly on the topics that you've shown interest in.
So, >>> I'd love it if we had a working group (or whatever) that focused on how to standardize how we train and deploy ML for production use. Count me in. Regards, Goran Goran S. Milovanović, PhD Data Scientist, Software Department Wikimedia Deutschland ------------------------------------------------ "It's not the size of the dog in the fight, it's the size of the fight in the dog." - Mark Twain ------------------------------------------------ On Thu, Feb 7, 2019 at 4:16 PM Andrew Otto <[email protected]> wrote: > Just came across > > https://www.confluent.io/blog/machine-learning-with-python-jupyter-ksql-tensorflow > > In it, the author discusses some of what he calls the 'impedance mismatch' > between data engineers and production engineers. The links to Ubers > Michelangelo <https://eng.uber.com/michelangelo/> (which as far as I can > tell has not been open sourced) and the Hidden Technical Debt in Machine > Learning Systems paper > <https://papers.nips.cc/paper/5656-hidden-technical-debt-in-machine-learning-systems.pdf> > are > also very interesting! > > At All hands I've been hearing more and more about using ML in production, > so these things seem very relevant to us. I'd love it if we had a working > group (or whatever) that focused on how to standardize how we train and > deploy ML for production use. > > :) > _______________________________________________ > Analytics mailing list > [email protected] > https://lists.wikimedia.org/mailman/listinfo/analytics >
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