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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

Moving beyond KPIs

The process of selecting KPIs, in general, should be relatively easy, as it is likely obvious what metrics are the best indicators (if online sales are down, then the application is likely not working). But if we want to get a more holistic view of what may be contributing to an operational problem, we must expand our analysis beyond the KPIs to indicators that emanate from the underlying systems and technology that support the application.

Fortunately, there are a plethora of ways to collect all kinds of data for centralization in the Elastic Stack. The Elastic Agent, for example, is a single, unified agent that you can deploy to hosts or containers to collect data and send it to the Elastic Stack. Behind the scenes, the Elastic Agent runs the Beats shippers or Elastic Endpoint required for your configuration. Starting from version 7.11, the Elastic Agent is managed in Kibana in the Fleet user interface and can be used to add and manage integrations for popular...

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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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