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Machine Learning with the Elastic Stack - Second Edition

You're reading from  Machine Learning with the Elastic Stack - Second Edition

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Pages 450 pages
Edition 2nd Edition
Languages
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
Camilla Montonen Camilla Montonen
Profile icon Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
View More author details

Table of Contents (19) Chapters

Preface 1. Section 1 – Getting Started with Machine Learning with Elastic Stack
2. Chapter 1: Machine Learning for IT 3. Chapter 2: Enabling and Operationalization 4. Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
5. Chapter 3: Anomaly Detection 6. Chapter 4: Forecasting 7. Chapter 5: Interpreting Results 8. Chapter 6: Alerting on ML Analysis 9. Chapter 7: AIOps and Root Cause Analysis 10. Chapter 8: Anomaly Detection in Other Elastic Stack Apps 11. Section 3 – Data Frame Analysis
12. Chapter 9: Introducing Data Frame Analytics 13. Chapter 10: Outlier Detection 14. Chapter 11: Classification Analysis 15. Chapter 12: Regression 16. Chapter 13: Inference 17. Other Books You May Enjoy Appendix: Anomaly Detection Tips

Creating an alert with a watch

Prior to version 7.12, Watcher was used as the mechanism to alert on anomalies found by Elastic ML. Watcher is a very flexible native plugin for Elasticsearch that can handle a number of automation tasks and alerting is certainly one of them. In versions 7.11 and earlier, users could either create their own watch (an instance of an automation task in Watcher) from scratch to alert on anomaly detection job results or opt to use a default watch template that was created for them by the Elastic ML UI. We will first look at the default watch that was provided and then will discuss some ideas around custom watches.

Understanding the anatomy of the legacy default ML watch

Now that alerting on anomaly detection jobs is handled by the new Kibana alerting framework, the legacy watch default template (plus a few other examples) are memorialized in a GitHub repository here: https://github.com/elastic/examples/tree/master/Alerting/Sample%20Watches/ml_examples...

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