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

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 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 scores

Interpreting the results of Elastic ML's anomaly detection jobs first requires the ability to recognize the fact that there are several levels of scoring unusualness, expressed within the results. They are as follows:

Bucket-level (result_type:bucket): This level summarizes the results of the entirety of the anomaly detection job per time bucket. Essentially, it is a representation of how unusual that time bucket is, given the configuration of your job.
Influencer-level (result_type:influencer): This is used to better understand the most unusual entities (influencers) within a timespan.
Record-level (result_type:record): This is the most detailed information regarding every anomalous occurrence or anomalous entity within a time bucket. Again, depending on the job configuration (multiple detectors, splits, and so on), there can be many record-level documents per time bucket.

Additionally, to fully appreciate how scoring is done, we also need...