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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
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
LinkedIn
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
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
LinkedIn
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
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

Section 3 – Data Frame Analysis

This section covers the basics of data frame analysis, what it is useful for, and how you will be able to implement it. It covers various types of analysis and how they differ from each other. At the end, the section covers some useful tidbits that can be used to get the most out of Elastic ML.

This section covers the following topics:

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Authors (3)

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Profile icon Rich Collier
LinkedIn
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
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Profile icon Bahaaldine Azarmi
LinkedIn
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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