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. :)
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