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Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261

Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261

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


Scaling Jupyter Notebooks with Luciano Resende - TWiML Talk #261

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

ratings:
Length:
34 minutes
Released:
May 6, 2019
Format:
Podcast episode

Description

Today we kick off PyDataSci with Luciano Resende, an Open Source AI Platform Architect at IBM and part of the Center for Open Source Data and AI Technology. Luciano and I caught up to discuss his work on Jupyter Enterprise Gateway, a scalable way to share Jupyter notebooks and other resources in an enterprise environment. In our conversation, we discuss some of the challenges that arise using Jupyter Notebooks at scale, the role of open source projects like Jupyter Hub and Enterprise Gateway, and some potential reasons for investing in and building custom notebooks. We also explore some common requests from the community, such as tighter integration with git repositories, as well as the python-centricity of the vast Jupyter ecosystem. The complete show notes for this episode can be found at twimlai.com/talk/261. Check out the rest of the PyDataSci series at twimlai.com/pydatasci. Thanks to this weeks sponsor, IBM, for their support of the podcast! Visit twimlai.com/ibm to learn more about the IBM Data Science Community.   
Released:
May 6, 2019
Format:
Podcast episode

Titles in the series (100)

This Week in Machine Learning & AI is the most popular podcast of its kind. TWiML & AI caters to a highly-targeted audience of machine learning & AI enthusiasts. They are data scientists, developers, founders, CTOs, engineers, architects, IT & product leaders, as well as tech-savvy business leaders. These creators, builders, makers and influencers value TWiML as an authentic, trusted and insightful guide to all that’s interesting and important in the world of machine learning and AI. Technologies covered include: machine learning, artificial intelligence, deep learning, natural language processing, neural networks, analytics, deep learning and more.