Presented By O'Reilly and Cloudera
Make Data Work
31 May–1 June 2016: Training
1 June–3 June 2016: Conference
London, UK

Analytics for large-scale time series and event data

Ira Cohen (Anodot)
9:30–10:00 Wednesday, 1/06/2016
Hardcore data science
Location: Capital Suite 4 Level: Intermediate
Tags: real-time, iot
Average rating: ****.
(4.80, 5 ratings)

Prerequisite knowledge

Attendees should have a general understanding of machine-learning algorithms for time series data.


Time series and event data form the basis for real-time insights about the performance of businesses such as ecommerce, the IoT, and web services, but gaining these insights involves designing a learning system that scales to millions and billions of data streams. Ira Cohen outlines such a system that performs real-time machine learning and analytics on streams at massive scale.

Photo of Ira Cohen

Ira Cohen


Ira Cohen is a cofounder and chief data scientist at Anodot, where he’s responsible for developing and inventing the company’s real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

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Bernhard Schaefer
3/06/2016 15:17 BST

Thanks for all the insights!
Are the slides available somewhere?