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You're reading from Machine Learning with the Elastic Stack Expert techniques to integrate machine learning with distributed search and analytics

Product type Paperback

Published in Jan 2019

Publisher Packt

ISBN-13 9781788477543

Length 304 pages

Edition 1st Edition

Tools

Elasticsearch

Concepts

Machine Learning

Authors (2):

Bahaaldine Azarmi

Rich Collier

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Table of Contents (12) Chapters

Preface

Machine Learning for IT FREE CHAPTER

Installing the Elastic Stack with Machine Learning

Event Change Detection

IT Operational Analytics and Root Cause Analysis

Security Analytics with Elastic Machine Learning

Alerting on ML Analysis

Using Elastic ML Data in Kibana Dashboards

Using Elastic ML with Kibana Canvas

Forecasting

ML Tips and Tricks

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ML Tips and Tricks

As we wind down the content for this book, it occurred to us that there's still a plethora of good, bite-sized explanations, examples, and pieces of advice that didn't quite fit into the other chapters. It therefore made sense to give them a chapter all to themselves. Enjoy this potpourri of tips and tricks!

The following topics will be covered in this chapter:

Job groups
Ignoring time periods
Top-down alerting
Sizing ML deployments

The rest of the chapter is locked

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Authors (2)

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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Bahaaldine Azarmi

Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

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