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Tyler Whitehouse

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White Paper: Data Science on Any Infrastructure

Posted by Tyler Whitehouse on Oct 25, 2020 7:56:54 PM

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. 
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Topics: Data Science, Multi-Cloud, Hybrid Infrastructure

Webinar Recap: Data Science 2.0 and Scaling Remote Teams

Posted by Tyler Whitehouse on Jun 30, 2020 3:09:15 PM

This recaps our first webinar of June 23, 2020. It was fun and we wanted to give access to the video.

The webinar demoed creating portable and reproducible work in Jupyter and RStudio, as well as an easy system for transferring work between CPU and GPU resources. It further explained why decentralization, not centralization, is best for collaboration and productivity in data science.  The current remote work situation makes this decentralized approach even more critical.

In the webinar Dean (CTO) and Tyler (CEO):

  • Outlined the technical problems of collaboration and managing data science work;
  • Related this problem to cost and productivity concerns;
  • Explained "centralized vs decentralized" and why decentralization is better;
  • Explained how local automation can make decentralization robust & scalable;
  • Demonstrated Gigantum's Client + Hub model for scaling collaboration and productivity.

Decentralization means letting data scientists work across resources in a self-service fashion. For us, it also means container native, not just cloud native. It is that simple.

The key to decentralization is automation and a UI at the local level, not as a monolithic, managed cloud  service. We call this "Self Service SaaS", which is sort of a silly phrase but captures what we mean.

Self Service SaaS takes the good parts of the SaaS experience, i.e. nice UIs and automation around difficult tasks, and eliminates the bad parts, i.e. zero control over deployment and everything that entails.

Check out the video and let us know what you think. We love to talk about this stuff and we want to hear your story and your problems. You can watch by filling out the form below.

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Topics: Data Science, Containers, Git, Jupyter, RStudio