Born in academia, Jupyter notebooks are prevalent in both learning and research environments throughout the scientific community. Due to the widespread adoption of big data, AI, and deep learning frameworks, notebooks are also finding their way into the enterprise, which introduces a different set of requirements.
Alan Chin and Luciano Resende explain how to introduce Jupyter Enterprise Gateway into new and existing notebook environments to enable a “bring your own notebook” model while simultaneously optimizing resources consumed by the notebook kernels running across managed clusters within the enterprise. Along the way, they detail how to use different frameworks with Enterprise Gateway to meet the needs of data scientists operating within the AI and deep learning ecosystems.
Alan Chin is a contributor working on Jupyter Enterprise Gateway and an open source advocate at IBM. His previous roles include build and release engineer and test engineer in DB2 on z/OS. In a previous life, he served as a crew chief with the 15th in Hurlburt Field and 16th ESOS in Afghanistan. Alan holds a BS in computer science from San Jose State University. He resides in San Francisco with his wife and three furry children (two cats and a dog).
Luciano Resende is a senior technical staff manager (STSM) and open source data science and AI platform architect at IBM CODAIT (formerly Spark Technology Center). He’s a member of ASF, where he’s been contributing to open source for over 10 years. He contributes to various big data-related Apache projects around the Apache Spark ecosystem as well as Jupyter ecosystem projects, building a scalable, secure, and flexible enterprise data science platform.
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