How do you get to high-performance machine learning, achieving truly best-in-class results against the competitors in your application area? For example, how do large organizations with decades of experience in analytics such as global finance institutions or international physics projects achieve ultra-high detection rates, looking for ultra-high-value needles in vast haystacks? In this talk we will summarize the essential ingredients needed to create such state-of-the-art systems, including choices regarding cluster designs, data layout, data preparation, machine learning methods, and computational approaches. To illustrate the main points, real examples of machine learning computations on massive data will be demonstrated throughout the talk.
Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is an Associate Professor at Georgia Tech and CTO of Skytree, Inc. His research focuses on scaling up all of the major practical methods of machine learning (ML) to massive datasets. He began working on this problem at NASA in 1993 (long before the current fashionable talk of “big data”). His large-scale algorithms helped enable the Top Scientific Breakthrough of 2003, and have won a number of research awards. He served on the National Academy of Sciences Committee on the Analysis of Massive Data and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies.
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