Ripped Machine Learning with Distributed Computing

Both Machine Learning with Distributed Computing are relatively complex software architectures to wrap your head around. Through the years the solution stack has taken various forms, most of which have been difficult to set up and maintain. Today with the advent of tools like TensorFlow and Kubernetes, we can combine these technologies and stand on the shoulders of giants. Your ML solutions will not just be running, but will also be easier to maintain and observe.

The session will present the fundamentals of how these two work together for a complementary solution stack. We walk through a hand-on demonstration that you can later take and exercise for yourself and show to your peers.

Prerequisite: Be sure to attend Kubernetes Koncepts (at least part 1) as this presentation builds on those ideas.