Scaling up systems for resource-intensive machine learning tasks demands convenient methods to manage computations distributed across multiple servers. Come and learn about both the processes underlying new Deep Learning techniques that have been applied to piloting drones and driving autonomous vehicles as well as the Docker containerization tools used to train these systems at scale.

If you don't know, docker has been around for a few years now to help with the deployment of applications using operating system level virtualization on Linux. It has a bunch of great features to help with this, but I would say the main use case for docker is that you can run any docker container on any docker host and it will run. "Docker containers run on any computer, on any infrastructure and in any cloud." Unfortunately that is wrong for deep learning applications. For any serious deep learning application, you need NVIDIA graphics cards, otherwise it could take months to train your models.