Machine Learning Applications for the Industrial Internet with Joseph Richards

Date: 
Friday, September 15, 2017 - 12:30
Source: 
University of San Francisco Seminar Series in Analytics
Attendees: 
175
City: 
San Francisco

Speaker: Joseph Richards, VP of Data & Analytics at GE Digital and head of the Wise.io Data Science Applications team


Deploying machine learning software applications for use cases in the Industrial Internet presents a unique set of challenges.  The sheer size, scale and value of data-driven problems at GE requires approaches that are highly accurate, robust, fast, scalable and, more than anything, fault tolerant.  At the same time, data-driven approaches to these use cases have the potential to drive billions of dollars in cost savings and to save thousands of lives.  In this presentation, I'll talk about my journey from academia to co-founding Wise.io as its head of data science to our acquisition by GE Digital.  I'll discuss our approach to building production-grade ML applications and will talk about our work across GE in industries such as Power, Aviation, Oil & Gas, and Healthcare.  I will cover a number of important characteristics of production-grade ML applications, such as repeatability, robustness, interpretability, computational efficiency and continuous learning and will share other lessons learned along the way.

Joseph (Joey) Richards is VP of Data & Analytics at GE Digital and head of the Wise.io Data Science Applications team.  His team is responsible for defining and implementing machine learning applications on behalf of GE and its customers.  Prior to joining GE, he was Co-founder and Chief Data Scientist at Wise.io (acquired by GE in 2016), where he built and deployed high-value ML applications for dozens of customers.  In his academic life, Joey was an NSF postdoctoral researcher in the Statistics and Astronomy departments at UC Berkeley and a Fulbright Scholar whose research focused on application of supervised and semi-supervised learning for problems in astrophysics; he holds a PhD in Statistics from Carnegie Mellon University.

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