You're reading from Machine Learning with the Elastic Stack Gain valuable insights from your data with Elastic Stack's machine learning features

Product type Paperback

Published in May 2021

Publisher Packt

ISBN-13 9781801070034

Length 450 pages

Edition 2nd Edition

Languages

Python

Tools

Elasticsearch

Concepts

Machine Learning

Authors (3):

Camilla Montonen

Rich Collier

Bahaaldine Azarmi

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Table of Contents (19) Chapters

Preface

1. Section 1 – Getting Started with Machine Learning with Elastic Stack

2. Chapter 1: Machine Learning for IT FREE CHAPTER

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...