How Technical Debt Could Leave Machine Learning Bankrupt

February 3, 2016 Nicole Hemsoth There has been a resounding uptick in attention around machine learning, but with relatively few large-scale systems in production (and even fewer public stories about progress and roadblocks), the wider story is all about the potential and dramatically less about the possibilities for problems. As we have covered here, building machine learning systems on the hardware front, while teeming with options, is not necessarily complex—at least in a relative sense. New tools, frameworks, and platforms are emerging constantly, reminiscent of the days when every company and startup had to have a hook that included the word “cloud” or “big data”. In many ways, machine learning, independent of its algorithmic developments over the years, is the product of both trends—mass attention can only be paid to…

Link to Full Article: How Technical Debt Could Leave Machine Learning Bankrupt

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