Upcoming Comet Industry Q&A: Collaborative Data Science and Machine Learning

Words By Austin Kodra

July 21, 2021

A conversation with Jakub Jurovych of Deepnote and Abubakar Abid of Gradio

We’re thrilled to share that we’re restarting our Industry Q&A series, and we’d love to have you join us (virtually, for now)—Monday, July 26th, at 11am ET. We’ll be hosting conversation with 2 industry leaders at the forefront of a new wave of tools for collaborative data science and machine learning.

Save Your Seat

About the Event

As we continue building tools to help data science teams track, visualize, and explain their machine learning experiments, we’ve noticed a renewed focus on effective collaboration as a core principle in accelerating ML development.

Given this reality, we wanted to chat with a couple of industry leaders at the forefront of making ML collaboration easier, more dynamic, and more implicit in team workflows. That’s why we’re thrilled to welcome Jakub Juryovych of Deepnote and Abubakar Abid of Gradio for a wide-ranging discussion about the essential nature of collaboration:

The paradigm shift towards collaboration in machine learning

What specific problems collaborative approaches can solve

️ How the industry is responding to the advent of team-based tools

And much more!

About the Guests

Jakub Jurovych is a founder and CEO of Deepnote. As an engineer, Jakub built tools for JavaScript development and worked on Firefox DevTools. Jakub studied Computer Science at Cambridge University, focusing on Human-Computer Interaction, Machine Learning and Computer Vision. Jakub is a YC alumnus (2019) and ranked in Forbes 30 Under 30. In 2019, Jakub founded Deepnote, a new kind of data science notebook.

Abubakar Abid is the CEO of Gradio. He received his Bachelor’s of Science in Electrical Engineering and Computer Science from MIT in 2015, and started his PhD at Stanford University in 2016. He’s also worked at Verily Life Sciences and Two Sigma. In his current role as CEO at Gradio, Abubakar is working to make machine learning models more reliable and effective in academic and business settings.

Register and Attend for Free

This event is free to attend, and will also be available on demand for those who register but cannot make the live session. We’ll also set aside some time for an audience Q&A, so be sure to bring questions you have.

We’d love to have you join us—and be on the lookout for more events like this in the future. We’re just getting started!