2015 was the year machine learning emerged from the academic closet. No longer was it an esoteric discipline commanded by the few, the proud, the data scientists. Now it was, in theory, everyone’s business.
2016 was the year theory became practice. Machine learning’s power and promise, and all that surrounded and supported it, moved more firmly into the enterprise development mainstream.
That movement revolved around three trends: new and improved tool kits for machine learning, better hardware (and easier access to it), and more cloud-hosted, as-a-service variants of machine learning that provided both open source and proprietary tools.