Allstate’s foundation is data. We extract value from our data by applying machine learning to make data-driven decisions. But machine learning is an intensive, iterative process; significant tradeoffs are made between data scientist time and model quality. By employing machine learning on Hadoop, iteration time is reduced and fewer tradeoffs are made, leading to a shortened development cycle and more highly optimized decisions. In this session, we discuss Allstate’s drive for better business results by using machine learning on Hadoop.
Ryan is a data scientist in Allstate’s Quantitative Research and Analytics department, where he uses big data to improve the customer experience.
Alexander Gray is CTO at Skytree and Associate Professor in the College of Computing at Georgia Tech. His work has focused on algorithmic techniques for making machine learning tractable on massive datasets. He began working with large-scale scientific data in 1993 at NASA’s Jet Propulsion Laboratory in its Machine Learning Systems Group. He recently served on the National Academy of Sciences Committee on the Analysis of Massive Data, as a Kavli Scholar, and a Berkeley Simons Fellow, and is a frequent advisor and speaker on the topic of machine learning on big data in academia, science, and industry.
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