Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
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Where does Jupyter fit into building end-to-end ML products?

Omoju Miller (GitHub)
11:50am–12:30pm Wednesday, March 27, 2019
Sponsored
 Location: 2014
Secondary topics:  Jupyter
Average rating: ***..
(3.50, 10 ratings)

What you'll learn

  • Learn how GitHub uses Jupyter notebooks for building ML products

Description

GitHub has a relatively nascent ML group. Its major challenge is to integrate ML product building processes into a mature product engineering org. This means that it’s responsible for building end-to-end ML products, from ETL to production. Omoju Miller details the many roles Jupyter notebooks play in the building of ML products, including Jupyter Server, notebooks as pull requests supports, and more.

Photo of Omoju Miller

Omoju Miller

GitHub

Omoju Miller is a machine learning engineer at GitHub. Omoju has over a decade of experience in computational intelligence. Apart from her work in AI, she has co-led the nonprofit investment in computer science education at Google and served as a volunteer advisor to the Obama administration’s White House Presidential Innovation Fellows. She is a member of the World Economic Forum Expert Network in AI. Omoju holds a PhD from UC Berkeley.