Abstract: 

I will discuss the challenges in building real world AI products in today's enterprise environment, and, in particular, the tradeoffs between "Discovery vs. Delivery." It seems every company today wants AI; but plug-and-play AI offerings are far and few between. I will describe the balance between the R&D necessary to create bespoke products and get them working within existing IT deployment environment. Topics will include cultural differences between IT and AI, how to scope a successful R&D project, data mining vs product development, model governance, existing deployment solutions, and testing.

Bio: 

Charles Martin holds a PhD in Theoretical Chemistry from the University of Chicago. He was then an NSF Postdoctoral Fellow and worked in a Theoretical Physics group at UIUC that studied the statistical mechanics of Neural Networks. He currently owns and operates Calculation Consulting, a boutique consultancy specializing in ML and AI, supporting clients doing applied research in AI. He maintains a well-recognized blog on practical ML theory and he has to date supported and performed the work on Implicit and Heavy Tailed Self Regularization in Deep Learning.