Jupyter and the Evolution of ML Tooling with Brian Granger

EPISODE 544

December 13, 2021

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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.

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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 been focused on making ML accessible to customers of all sizes and across industries, and over 100,000 of them trust AWS for machine learning and artificial intelligence services. AWS is constantly innovating across all areas of ML including infrastructure, tools on Amazon SageMaker, and AI services, such as Amazon CodeWhisperer, an AI-powered code companion that improves developer productivity by generating code recommendations based on the code and comments in an IDE. AWS also created purpose-built ML accelerators for the training (AWS Trainium) and inference (AWS Inferentia) of large language and vision models on AWS.

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.