Analytic workflows: JupyterHub, Binder and other open-source tools
7 Keble Road Oxford
How JupyterHub, Binder, and other open-source tools are moving analytics workflows to the web.
Modern-day workflows are moving onto the cloud. Every year it becomes easier to request computational resources from cloud providers that meet your every need, and researchers, educators, and companies are increasingly turning to the cloud to standardize their environment and provide an easy point-of-access to cloud resources. Jupyter is a web-savvy technology, making it straightforward to run from a cloud server. JupyterHub makes it possible to serve computational environments to _many _users who only need to access a website in order to start interacting with code and data. Binder builds off of JupyterHub, and makes it possible to create sharable, interactive computing environments with others. In this talk, I'll outline the guiding principles behind JupyterHub and Binder, and discuss ways in which they are already used in education, publishing, and data analytics. I'll discuss some of the technology behind these projects, and give an idea for where they're going next.
Chris Holdgraf is a Community Architect and Data Science Fellow at the Berkeley Institute for Data Science and a member of the Data Science Education Program at UC Berkeley. His background is in cognitive and computational neuroscience, where he used predictive models to understand the auditory system in the human brain. He's interested in the boundary between technology, open-source software, and scientific workflows, as well as creating new pathways for this kind of work in science and the academy. He's a core member of Project Jupyter, specifically working with JupyterHub and Binder, which both aim to make it easier for researchers and educators to do their work in the cloud.
He works on these core tools, along with research and educational projects that use these tools at Berkeley and in the broader open science community.