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

Summary

Anomaly detection jobs are certainly useful on their own, but when combined with near real-time alerting, users can really harness the power of automated analysis – while also being confident about getting only alerts that are meaningful.

After a practical study of how to effectively capture the results of anomaly detection jobs with real-time alerts, we went through a comprehensive example of using the new Kibana alerting framework to easily define some intuitive alerts and we tested them with a realistic alerting scenario. We then witnessed how an expert user can leverage the full power of Watcher for advanced alerting techniques if Kibana alerting cannot satisfy the complex alerting requirements.

In the next chapter, we'll see how anomaly detection jobs can assist not only with alerting on important key performance indicators but also how Elastic ML's automated analysis of a broad set of data within a specific application context is the means to achieving...