As volumes of data increase, manually searching and visualising consumer or user behaviours becomes more and more difficult. An alternative approach is to use machine learning to automatically build behavioural models of these behaviours. These models enable users to gain deep insights into behavioural characteristics that are beyond the capabilities of classical search techniques.
Typical use cases include, automatically understanding users that are behaving unusually and understanding the typical behaviour of the population.
This talk will present real examples of machine learning techniques applied to real-time behavioural data, and describe the methodology behind these methods along with an overview of the machine learning space.
Stephen Dodson, PhD, Tech Lead, Machine Learning at Elastic
Steve was formerly founder and CTO of Prelert, a London based software company that developed novel unsupervised machine learning technologies to identify anomalies in IT Ops and IT Security data. Prelert was acquired by Elastic in September 2016, and Steve continues to grow and lead the machine learning group at Elastic.
Prior to Prelert, Steve was a founding member of Riversoft (IPO’ed and acquired by Micromuse) and Njini (acquired by Riverbed). At Riversoft, he led the design of the topology driven root-cause analysis technology used today within IBM Tivoli Netcool, HP OpenView, and Cisco management tools.
Prior to software development, Steve worked in the Computational Mechanics group at Imperial College, London where he delivered key contributions to the field, resolving scalability issues using a novel approach to solving Maxwell’s equations which allowed it to become a practical technique used today by major companies. Steve holds MEng in Mechanical Engineering and a PhD in Computational Methods from Imperial College, London alongside a CES from École Centrale de Lyon.
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