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.
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.
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