Now’s the time to do deep learning in the cloud


The AWS Re:invent conference is coming up, and predictions are starting to fly around what Amazon Web Services will announce there. A sure bet is that it will announce some sort of deep learning cloud service. Of course, Google, Microsoft, and IBM won’t be far behind; indeed, both IBM and Microsoft have their own special deep learning projects in the works, called Brainwave and Distributed Deep Learning, respectively.

So, what’s the difference between machine learning and deep learning? Simply put, machine learning typically deals with tactical applications of AI, such as making instant predictions. Deep learning provides a foundation for the understanding of massive amounts of patterns or data.

; these provide the basic AI capabilities that enterprises need. Just as with deep learning, the cloud has brought AI back from the grave, because we now have the capability to lease compute and storage on the cheap.

But there are also opportunities for deep learning to enhance an enterprise’s ability to do what enterprises do, with more accuracy and, most important, with the ability to build knowledge through pattern or data observation. Even better, deep learning systems get better over time, typically much better than a team of experts.