Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA

Machine learning applications for the industrial internet

Joseph Richards (GE Digital)
4:20pm–5:00pm Wednesday, March 7, 2018
Data science and machine learning
 Location: LL20 A
Secondary topics:  Graphs and Time-series
Average rating: *****
(5.00, 1 rating)

Who is this presentation for?

  • Data scientists

Prerequisite knowledge

  • A basic understanding of machine learning concepts

What you'll learn

  • Explore GE's approach to production-grade machine learning

Description

Deploying ML software applications for use cases in the industrial internet presents a unique set of challenges. Data-driven problems require approaches that are highly accurate, robust, fast, scalable, and fault tolerant. However, such approaches have the potential to drive billions of dollars in cost savings and save thousands of lives.

Joseph Richards shares GE’s approach to building production-grade ML applications and explores work across GE in industries such as power, aviation, and oil and gas. Along the way, Joseph covers a number of important characteristics of production-grade ML applications, such as repeatability, robustness, interpretability, computational efficiency, and continuous learning, and discusses lessons learned deploying ML applications at GE.

Photo of Joseph Richards

Joseph Richards

GE Digital

Joseph (Joey) Richards is vice president of data and analytics at GE Digital and head of the Wise.io data science applications team, which is responsible for defining and implementing machine learning applications on behalf of GE and its customers. Previously, he was cofounder 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; an NSF postdoctoral researcher in the Statistics and Astronomy Departments at UC Berkeley; and a Fulbright Scholar whose research focused on the applications of supervised and semisupervised learning for problems in astrophysics. Joey holds a PhD in statistics from Carnegie Mellon University.