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

Enabling Elastic ML features

The process for enabling Elastic ML features inside the Elastic Stack is slightly different if you are doing so within a self-managed cluster versus using the Elasticsearch Service (ESS) of Elastic Cloud. In short, on a self-managed cluster, the features of ML are enabled via a license key (either a commercial key or a trial key). In ESS, a dedicated ML node needs to be provisioned within the cluster in order to utilize Elastic ML. In the following sections, we will explain the details of how this is accomplished in both scenarios.

Enabling ML on a self-managed cluster

If you have a self-managed cluster that was created from the downloading of Elastic's default distributions of Elasticsearch and Kibana (available at elastic.co/downloads/), enabling Elastic ML features via a license key is very simple. Be sure to not use the Apache 2.0 licensed open source distributions that do not contain the X-Pack code base.

Elastic ML, unlike the bulk...