Anomaly Detection 101 - Elizabeth (Betsy) Nichols Ph.D.
This presentation surveys a collection of techniques for detecting anomalies in a DevOps environment. Each of the techniques has strengths and weaknesses that are illustrated via real-world (anonymized) customer data. Techniques discussed include deterministic and statistical models as well as uni-variate and multi-variate analytics. Examples are given that show concrete evidence where each can succeed and each can fail. This presentation is about concepts and how to think about alternative anomaly detection techniques. This presentation is not an academic discourse in math, statistics or probability theory. Elizabeth A. Nichols (Betsy) is Chief Data Scientist at Netuitive, Inc. In this role she is responsible for leading the company's vision and technologies for analytics, modeling, and algorithms. Betsy has applied mathematics and computer technologies to create systems for war gaming, space craft mission optimization, industrial process control, supply chain logistics, electronic trading, advertising networks, IT security and risk models, and network and systems management. She has co-founded three companies, all of which delivered analytics to commercial and government enterprises. Betsy graduated with an A.B. from Vassar College and a Ph.D. in Mathematics from Duke University. Check her out on LinkedIn ( / elizabethanichols ) for more information.
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