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

Results index schema details

As we have already hinted, inside the results index, there are a variety of different documents, each with their own usefulness with respect to understanding the results of the anomaly detection jobs. The ones we will discuss in this section are the ones that directly relate to the three levels of abstraction that we discussed previously in this chapter. They are aptly named as follows:

result_type:bucket: To give bucket-level results
result_type:record: To give record-level results
result_type:influencer: To give influencer-level results

The distribution of these document types will depend on the ML job configuration and the characteristics of the dataset being analyzed. These document types are written with the following heuristic:

result_type:bucket: One document is written for every bucket span's worth of time. In other words, if the bucket span is 15 minutes, then there will be one document of this type being written...