Jupyter and the Evolution of ML Tooling with Brian Granger

EPISODE 544

December 13, 2021

WATCH

Play Video

Join our list for notifications and early access to events

About this Episode

Today we conclude our AWS re:Invent coverage joined by Brian Granger, a senior principal technologist at Amazon Web Services, and a co-creator of Project Jupyter. In our conversion with Brian, we discuss the inception and early vision of Project Jupyter, including how the explosion of machine learning and deep learning shifted the landscape for the notebook, and how they balanced the needs of these new user bases vs their existing community of scientific computing users. We also explore AWS’s role with Jupyter and why they’ve decided to invest resources in the project, Brian's thoughts on the broader ML tooling space, and how they’ve applied (and the impact of) HCI principles to the building of these tools. Finally, we dig into the recent Sagemaker Canvas and Studio Lab releases and Brian’s perspective on the future of notebooks and the Jupyter community at large.

Connect with Brian

Thanks to our sponsor Amazon Web Services

You know AWS as a cloud computing technology leader, but did you realize the company offers a broad array of services and infrastructure at all three layers of the machine learning technology stack? AWS has helped more than 100,000 customers of all sizes and across industries to innovate using ML and AI with industry-leading capabilities and they’re taking the same approach to make it easy, practical, and cost-effective for customers to use generative AI in their businesses. At the bottom layer of the ML stack, they’re making generative AI cost-efficient with Amazon EC2 Inf2 instances powered by AWS Inferentia2 chips. At the middle layer, they’re making generative AI app development easier with Amazon Bedrock, a managed service that makes pre-trained FMs easily accessible via an API. And at the top layer, Amazon CodeWhisperer is generally available now, with support for more than 10 programming languages.

To learn more about AWS ML and AI services, and how they’re helping customers accelerate their machine learning journeys, visit twimlai.com/go/awsml.