A bunch of experienced Google engineers recently published a paper at a NIPS workshop titled "Machine Learning: The High Interest Credit Card of Technical Debt". This is a great paper that distills several dozen years worth of experience in building and maintaining a large-scale, complex, industrial-strength machine-learning system. If you haven't seen this paper I highly recommend it to get a sense for the patterns and anti-patterns they've observed along with supporting anecdotes.
The perspective is very different from a typical ML paper -- you're not going to see a new model or a clever optimization strategy. The focus is mostly on the places where you have to go beyond the generally understood good software engineering practices to make the job of operating a large-scale ML system manageable. It's a short and fun read. Even if you've run an industrial ML system for a few years, you'll likely learn a trick or two from this paper.
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