Keeping pace with new technologies for data science and machine learning can be overwhelming. There is a plethora of open source options, and it's a challenge to get these tools up and running easily and consistently in a large-scale distributed environment.

One of the most popular options today is distributed TensorFlow using the Spark framework and GPU hardware acceleration. This session will discuss how to get up and running quickly with TensorFlow and Spark for deep learning, running on containers with a shared pool of GPU resources.

Find out how you can spin up (and tear down) GPU-enabled TensorFlow and Spark clusters on-demand, with just a few mouse clicks.