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Machine Learning with the Elastic Stack - Second Edition

You're reading from Machine Learning with the Elastic Stack - Second Edition

Product type Book

Published in May 2021

Publisher Packt

ISBN-13 9781801070034

Pages 450 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Authors (3):

Rich Collier

Camilla Montonen

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

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...

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Authors (3)

Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.

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Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.

See other products by Camilla Montonen

Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.

See other products by Bahaaldine Azarmi

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