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Google Rules of Machine Learning (2018)
Best Practices for ML Engineering Martin Zinkevich This document is intended to help those with a basic knowledge of machine learning get the benefit of Google's best practices in machine learning. It presents a style for machine learning, similar to the Google C++ Style Guide and other popular guides to practical programming.
Insofar as well-being and company health is concerned, human judgement is required to connect any machine learned objective to the nature of the product you are selling and your business plan. While a change which is obviously bad should not be used, anything that looks reasonably near production should be tested further, either by paying laypeople to answer questions on a crowdsourcing platform, or through a live experiment on real users. Thanks to David Westbrook, Peter Brandt, Samuel Ieong, Chenyu Zhao, Li Wei, Michalis Potamias, Evan Rosen, Barry Rosenberg, Christine Robson, James Pine, Tal Shaked, Tushar Chandra, Mustafa Ispir, Jeremiah Harmsen, Konstantinos Katsiapis, Glen Anderson, Dan Duckworth, Shishir Birmiwal, Gal Elidan, Su Lin Wu, Jaihui Liu, Fernando Pereira, and Hrishikesh Aradhye for many corrections, suggestions, and helpful examples for this document.
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