How to train and deploy deep learning at scale

In five lines, you can describe how your architecture looks and then you can also specify what algorithms you want to use for training. There are a lot of other systems challenges associated with actually going end to end, from data to a deployed model. The existing software solutions don't really tackle a big set of these challenges. For example, regardless of the software you're using, it takes days to weeks to train a deep learning model. There's real open challenges of how to best use parallel and distributed computing both to train a particular model and in the context of tuning hyperparameters of different models. We also found out the vast majority of organizations that we've spoken to in the last year or so who are using deep learning for what I'd call mission-critical problems, are actually doing it with on-premise hardware.