Productized Automated Model Building: How to Go From Data to Deployment with Neuroevolution
Productized Automated Model Building: How to Go From Data to Deployment with Neuroevolution

Abstract: 

In the world of data operationalization, the ability to go from data to deployment quickly is paramount. In this session, Keith Moore, Director of Product Management at SparkCognition, covers how the understanding of your data prior to model building can be accelerated, how neural architecture search works, and why deploying models doesn't need to be as hard as it is today. This session will discuss why common neural network architectures may work well for known, established data problems, but fall short when modern machine learning applications demand more performance and higher levels of sophistication. He will take you through the journey their team faced on productizing better models, explain what most data-driven organizations really care about, and share some of the technology problems that are yet to be solved.

Bio: 

Keith Moore is the Director of Product Management at SparkCognition and is responsible for the development of the IoT product line (SparkPredict®). He specializes in applying advanced data science and natural language processing algorithms to complex data sets.
Moore previously worked for National Instruments as an analog-to-digital converter and vibration software product manager. Prior to that, he developed client software solutions for major oil and gas, aerospace, and semiconductor organizations.
Moore has served as a board member of Pi Kappa Phi fraternity, and still serves volunteers on the alumni engagement committee. He graduated from the University of Tennessee with a with a B.A. in mechanical engineering.