Why there are no shortcuts to machine learning

Across all companies, product managers tend to define project success metrics (36 percent), with executive management (29 percent) and data science teams (21 percent) also involved.But for experienced companies, while product managers still get cited most (34 percent), data science leads (27 percent) are roughly equal with executive staff (28 percent).The least experienced companies tend to look to executive management (31 percent) and rarely to their data science leads (16 percent)..That wouldn’t be a problem but for the fact that those data science teams are best positioned to figure out how to use the data and to measure its success.Too often, it’s the blind leading the blindThis reliance on executive management to drive data science calls to mind the surveys that show executives calling themselves data-driven but then ignoring data that doesn’t support decisions prompted by gut instinct (62 percent admit to doing this).Enterprises that lack big-data savvy seem to want to pay lip service to data, but they don’t understand the nuances of effective data science..They simply lack the requisite experience to ensure that they’re gleaning meaningful, unbiased insights to data.More sophisticated enterprises grasp what Gartner’s Andrew White meanswhen he talks about understanding machine learning models and how that can breed trust in the results:What is new [with AI] is that AI is able to redraw the line — what was thought of us too complex and not routine can now be exploited with AI..AI can (so the promise goes) cope with more complex and more cognitive work than previous technologies.This new reality will only survive the light of day if the outcome of the automated work left to AI make sense..If the new-fangled black box takes decisions and changes outcomes that humans don’t understand, those humans will likely turn off the box..So, understanding the decision to some degree is very important.However, understanding or interpreting a decision is quite different to understanding how the algorithm works..A human should be able to grasp the principles of inputs, choice, weights, and results, even if an algorithm combines many of these to an extent that we cannot even prove the process..If the gap between outcome and approximate inputs are too varied, trust in the algorithm will likely fail — that’s just human nature.Getting to this level of understanding can’t be bought for the price of a consultant..Nor does it come ready-made in the cloud..Tools like Google’s AutoML purport to “enable developers with limited machine learning expertise to train high-quality models specific to their business needs.” This sounds great, but so much of the benefits that derive from data science require to experience with data science..It’s not just a matter of tuning a model, but rather knowing how to do so, which is born in the trial and error of experience.Additionally, doing data science right requires a cultural mindset that, again, comes with experience..There are no shortcuts.. More details

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