“A machine learning research paper tends to present a newly proposed method or algorithm in relative isolation. Problem context, data preparation, and feature engineering are hopefully discussed to the extent required for reader understanding and scientific reproducibility, but are usually not the primary focus. Given the goals and constraints of the format, this can be seen as a reasonable trade-off: the authors opt to spend scarce “ink” on only the most essential (often abstract) ideas.
As a consequence, implementation details relevant to the use of the proposed technique in an actual production system are often not mentioned whatsoever. This aspect of machine learning is often left as “folk wisdom” to be picked up from colleagues, blog posts, discussion boards, snarky tweets, open-source libraries, or more often than not, first-hand experience.
Papers from conference “industry tracks” often deviate from this template, yielding valuable insights about what it takes to make machine learning effective in practice. This paper from Google on detecting “malicious” (ie, scam/spam) advertisements won best industry paper at KDD 2011 and is a particularly interesting example…”
http://blog.david-andrzejewski.com/machine-learning/practical-machine-learning-tricks-from-the-kdd-2011-best-industry-paper/
