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 API

If programmatic access to the results is your thing, in addition to querying the results indices directly, you could opt to instead query Elastic ML's results API. Some parts of the API are redundant to what we've already explored, and some parts are unique. We will now check them out in the upcoming sections.

Results API endpoints

There are five different results API endpoints available:

Get buckets
Get influencers
Get records
Get overall buckets
Get categories

The first three API endpoints give results that are redundant in light of what we've already covered in this chapter by way of querying the results index directly (through Kibana or using the Elasticsearch _search API), and that method actually allows more flexibility, so we really won't bother discussing them here. However, the last two API endpoints are novel, and each deserves an explanation.