3 ways to massively fail with machine learning (and one key to success) - TechRepublic

Though everyone seems to be piling on the machine learning bandwagon, it's a game that only the rich can play, as I've written. While open source machine learning projects like Google's TensorFlow and Amazon's DSSTNE lower the bar to would-be machine learning engineers, resolving the skills deficit that Gartner analyst Merv Adrian called the biggest hurdle to machine learning success, no amount of training can resolve a thornier issue: Lack of data. Yandex, the Google of Russia, has plenty of data, coupled with experience wrangling it to machine learning success. It's therefore fascinating to hear Yandex COO Alexander Khaytin talk through the best ways to bridge the data divide that keeps the vast majority of enterprises from achieving machine learning success. But first, you're going to need data.