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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

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.

Getting the overall buckets API

The overall buckets API...

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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.

See other products by Rich Collier

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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