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

Anomaly detection in Elastic APM

Elastic APM takes application monitoring and performance management to a whole new level by allowing users to instrument their application code to get deep insights into the performance of individual microservices and transactions. In complex environments, this could generate a large number of measurements and poses a potentially paradoxical situation – one in which greater observability is obtained via this detailed level of measurement while possibly overwhelming the analyst who has to sift through the results for actionable insights.

Fortunately, Elastic APM and Elastic ML are a match made in heaven. Anomaly detection not only automatically adapts to the unique performance characteristics of each transaction type via unsupervised machine learning, but it can also scale to handle the possibly voluminous amounts of data that APM can generate.

While the user is always free to create anomaly detection jobs against any kind of time-series...