Robert Grossman discusses some lessons learned moving machine-learning algorithms usually run manually by data scientists to operational environments where they run automatically on the new data that arrives each day. Robert offers three case studies in order to extract several techniques that have consistently proved useful and discuss how best these techniques can be used in practice: the first case study deals with the development of a system to analyze genomic datasets; the second describes the development of a system for the daily processing of new hyperspectral images to look for patterns of interest; and the third involves the incremental improvement of an algorithm for change detection run on streaming data.
Topics from these case studies include:
Robert Grossman is a faculty member and the chief research informatics officer in the Biological Sciences Division of the University of Chicago. Robert is the director of the Center for Data Intensive Science (CDIS) and a senior fellow at both the Computation Institute (CI) and the Institute for Genomics and Systems Biology (IGSB). He is also the founder and a partner of the Open Data Group, which specializes in building predictive models over big data. Robert has led the development of open source software tools for analyzing big data (Augustus), distributed computing (Sector), and high-performance networking (UDT). In 1996, he founded Magnify, Inc., which provides data-mining solutions to the insurance industry and was sold to ChoicePoint in 2005. He is also the chair of the Open Cloud Consortium, a not-for-profit that supports the research community by operating cloud infrastructure, such as the Open Science Data Cloud. He blogs occasionally about big data, data science, and data engineering at Rgrossman.com.
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