Jupyter and the Evolution of ML Tooling with Brian Granger

800 800 The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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!

I want to send a huge thanks once again to our friends at Amazon Web Services for their support of the podcast and their sponsorship of this year’s re:Invent series.

AWS offers a broad array of services and infrastructure at all three layers of the machine learning technology stack, ranging from high performance compute instances running the latest GPUs and custom-designed silicon for training and inference, to the Amazon SageMaker family of services that allows data scientists and developers to quickly build, train, and deploy high-quality models, to a full complement of AI services for use cases like demand forecasting, fraud prevention, personalized recommendations, document analysis, and much more.

More than 100,000 customers trust AWS for Machine Learning, and the company aims to put machine learning in the hands of every developer, data scientist and enthusiast with newly announced offerings like Amazon SageMaker Studio Lab which offers free JupyterLab Notebooks, GPU Compute and Storage for your machine learning projects.

To learn more about AWS machine learning services, and how they’re helping customers accelerate their machine learning journeys, visit aws.amazon.com/machine-learning/

And also be sure to check out studiolab.sagemaker.aws to sign up for SageMaker Studio Lab.

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