Jupyter and the Evolution of ML Tooling with Brian Granger

EPISODE 544
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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

AWS offers a broad array of services and infrastructure at all three layers of the machine learning technology stack. More than 100,000 customers trust AWS for Machine Learning, and the company aims to put ML in the hands of every developer, data scientist, and enthusiast with newly announced offerings like Amazon CodeWhisperer, a new ML-powered pair programmer that helps developers improve productivity by reducing the time to build software applications significantly.

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

And also be sure to check out stage.twimlai.net/go/sagemaker to sign up for SageMaker Studio Lab.

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