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Published on Dec 15, 2014
Real-time analytics represent the frontier of Big Data analysis. A convergence of technologies and statistical methodology are now making it impossible to extract live insights from real-time data streams, across a broad spectrum of use cases. For this technological wave to reach its full potential, however, capabilities must rise above simple queries and event processing, to full-scale real-time machine learning. This presents significant challenges, most notably the need for online efficient model updates, and the ability to behave robustly against unforeseen changes in the data distribution. In this webinar, we present a selective overview of the challenges, and available methodology for streaming machine learning, across both academia and industry.
Dr Christoforos Anagnostopoulos
Christoforos holds a BA in Mathematics from Cambridge University and a PhD in Mathematics from Imperial College, specializing in intelligent streaming data analysis. He is a Lecturer in Statistics at the Department of Mathematics in Imperial College, where he teaches advanced statistics. His research is in computational statistics with particular focus on streaming datasets. Christoforos is the co-Founder and the Chief Science Officer of Mentat Innovations.