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When it comes to GPUs, the "cloud vs on-prem" debate is resolved. The answer is just do both. 

While that sounds crazy, it is now possible.

The first part of the story is an emerging class of GPU enabled "data science workstations", i.e. evolved & leaner versions of gaming & design machines. For roughly the price of a high end Mac, vendors (Dell, HP & Lenovo ) now offer laptops with a powerful NVIDIA GPU that you can use whenever & wherever you want.

Data scientists waste a lot of time.

Data science is rife with technical challenges. Many of these problems have nothing to do with the job of a data scientist. Processing speed, sharing memory, security issues, package management, etc are all part of data science considerations but are not core to what we do. They are merely necessary steps along the way to doing productive data work. Solve these issues and data scientists immediately have more time to work.

This paper outlines a new system for managing data science work & collaboration across machines. The system provides the self-determination & flexibility for open source software and approaches necessary for innovation while eliminating the chaos that is typical of "do it yourself" systems. 

In particular, the paper outlines the challenges of scaling team data science and introduces a low cost, turnkey framework that provides:

• Easy deployment on premises or in the cloud 
• Reduced IT effort for provisioning & managing infrastructure and environments
• One click transfer of customized & reproducible Python or R work between machines & locations
• Automation and streamlining for versioning, containerization and best practices. 

Pretty much every enterprise needs a data science platform that:

1. Provides productivity and collaboration features for individuals and teams;
2. Streamlines the creation & distribution of Python and R environments;
3. Lightens the technical load on data scientists and IT staff;
4. Runs across bare metal, private cloud, and public clouds.