jm + best-practices + google   4

'Software Engineering at Google'
20 pages of Google's software dev practices, with emphasis on the build system (since it was written by the guy behind Blaze). Naturally, some don't make a whole lot of sense outside of Google, but still some good stuff here
development  engineering  google  papers  software  coding  best-practices 
february 2017 by jm
'Rules of Machine Learning: Best Practices for ML Engineering' from Martin Zinkevich
'This document is intended to help those with a basic knowledge of machine learning get the benefit of best practices in machine learning from around Google. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming. If you have taken a class in machine learning, or built or worked on a machine­-learned model, then you have the necessary background to read this document.'

Full of good tips, if you wind up using ML in a production service.
machine-learning  ml  google  production  coding  best-practices 
january 2017 by jm
Practical machine learning tricks from the KDD 2011 best industry paper
Wow, this is a fantastic paper. It's a Google paper on detecting scam/spam ads using machine learning -- but not just that, it's how to build out such a classifier to production scale, and make it operationally resilient, and, indeed, operable.

I've come across a few of these ideas before, and I'm happy to say I might have reinvented a few (particularly around the feature space), but all of them together make extremely good sense. If I wind up working on large-scale classification again, this is the first paper I'll go back to. Great info! (via Toby diPasquale.)
classification  via:codeslinger  training  machine-learning  google  ops  kdd  best-practices  anti-spam  classifiers  ensemble  map-reduce 
july 2012 by jm

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