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

Summary

In this section, we have dipped our toes into the world of Data Frame Analytics, a whole new branch of machine learning and data transformation tools that unlock powerful ways to use the data you have stored in Elasticsearch to solve problems. In addition to giving an overview of the new unsupervised and supervised machine learning techniques that we will cover in future chapters, we have studied three important topics: transforms, using the Painless scripting language, and the integration between Python and Elasticsearch. These topics will form the foundation of our future work in the following chapters.

In our exposition on transforms, we studied the two components – the pivot and aggregations – that make up a transform, as well as the two possible modes in which to run a transform: batch and continuous. A batch transform runs only once and generates a transformation on a snapshot of the source index at a particular point in time. This works perfectly for...