7–9 November 2016: Conference & Tutorials
9–10 November 2016: Training
Amsterdam, The Netherlands
Arun Kejariwal

Arun Kejariwal
Lead Engineer, Independent

@arun_kejariwal

Arun Kejariwal is an independent lead engineer. Previously, he was he was a statistical learning principal at Machine Zone (MZ), where he led a team of top-tier researchers and worked on research and development of novel techniques for install-and-click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns, and his team built novel methods for bot detection, intrusion detection, and real-time anomaly detection; and he developed and open-sourced techniques for anomaly detection and breakout detection at Twitter. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Sessions

14:40–15:20 Tuesday, 8/11/2016
Metrics/monitoring Cloud, DevOps G102/103 Audience level: Intermediate
Arun Kejariwal (Independent)
Average rating: **...
(2.78, 9 ratings)
Data-driven decision making has become a norm in the industry. In light of this—coupled with the high volume and velocity of data streams—large clusters are used to store and analyze data. However, deriving actionable insights from the data chest has been a daunting task. Arun Kejariwal presents approaches for analyzing operations data in the presence of “holes” in the time series. Read more.