Over the past decade Deep Learning has revolutionized much of Data Mining and Artificial Intelligence. Several factors have contributed to this virtuous cycle, primarily the ready availability of data in the cloud and a shift in the hardware resources that can be used for computation, mostly away from memory intensive models to compute intensive ones. For instance, large amounts of image and video data are available thanks to cheap and ubiquitous sensors. Processing them is only possible with equally copious amounts of low-precision computation. At the same time, expressive machine learning frameworks have allowed statistical modelers to design complex models with ease and to deploy them at scale, thus increasing the demand for computation even further. In this talk I will illustrate how these interaction cycles are likely to shape machine learning in the future.