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If you’re not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms’ accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build.
But breakthroughs in deep learning aren’t the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to non-experts, opening up access to a vast group of people.
For most software developers, there have historically been many barriers to entry in machine learning, most notably software libraries designed more for academic researchers than for software engineers as well as a lack of sufficient data. With massive increases in the data being generated and stored by many applications, though, the set of companies with data sets on which machine learning algorithms could be applied has significantly expanded.
In tandem, the last few years have seen a proliferation of cutting-edge, commercially usable machine learning frameworks, including the highly successful scikit-learn Python library and well-publicized releases of libraries like Tensorflow by Google and CNTK by Microsoft Research. The last two years have also seen the major cloud providers Amazon Web Services and Google Cloud Services release machine learning–specific services — both Machine Learning as a Service platforms and graphics processor unit machines optimized for machine learning work.