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Machine Learning With Go

You're reading from  Machine Learning With Go

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781785882104
Pages 304 pages
Edition 1st Edition
Languages

Table of Contents (11) Chapters

Preface 1. Gathering and Organizing Data 2. Matrices, Probability, and Statistics 3. Evaluation and Validation 4. Regression 5. Classification 6. Clustering 7. Time Series and Anomaly Detection 8. Neural Networks and Deep Learning 9. Deploying and Distributing Analyses and Models 10. Algorithms/Techniques Related to Machine Learning

Anomaly detection

As mentioned in the introduction to this chapter, we might not always be interested in forecasting a time series. We might want to detect anomalous behavior in a time series. For example, we might want to know when out of the ordinary bursts of traffic come across our network, or we may want an alert when out of the ordinary numbers of users are attempting certain things inside of our application. These events could be tied to security concerns or may just be used to adjust our infrastructure or application settings.

Thankfully, due to Go's history of usage in monitoring and infrastructure, there are a variety of Go-based options to detect anomalies in time series data. This tooling has been used in production to detect anomalous behavior while monitoring infrastructure and applications and, although there are more tools than can be mentioned here, I will...

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