A Day In The Life Of A Corporate Data Science Platform

by Lucas Durand

Machine Learning & Data Science

Providing an end-user computing environment for on-demand Data Science capabilities in a heavily regulated industry comes with inherent challenges. Not only are we trying to appeal to industry professionals from different backgrounds, from software developers to machine learning and analytics experts, we also want to appeal to first-time users that might not have the permissions or know-how to set up a local python installation. All of them are important members of the growing python community with different goals and needs. We think the answer to this is to give everyone python, no questions asked. Regulatory concerns are handled with a custom implementation of JupyterHub, which we also extend to form the backbone of a complete solution delivery pipeline. In this talk we tackle hands-on examples of how to implement logging, monitoring, and app deployments from within a notebook context

About the Author

Lucas is a self-taught pythonista working at TD Securities in the data science space. His work has a big focus on empowering users and building tools that make rapid prototyping a reality. Before that, he was exploring particle astrophysics simulations of dark matter. He tries to put as much of his work up on github, including these slides: github.com/lucasdurand

Talk Details

Date: Sunday Nov. 17

Location: Round Room

Begin time: 16:05

Duration: 25 minutes