Machine Learning with Elasticsearch
Submitted by Aravind Putrevu (@aravindputrevu) on Wednesday, 11 April 2018
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.
- Overview of holy grail
- ML Leaps
- What is in store from Elastic?
- What it can/can’t do?
Aravind is a loquacious person, who has something to talk about everything. He is passionate about evangelising technology, meeting developers and helping in solving their problems. He is a backend developer and has six years of backend development experience.
Previously, He worked at McAfee Antivirus as a Sr. Software Engineer in Cloud Security Domain. He has deep interest in Search, Machine Learning, Security Incident Analysis and IoT tech. In his free time, he plays around Raspi or a Arduino.