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Machine Learning with the Elastic Stack

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

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Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781788477543
Length 304 pages
Edition 1st Edition
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Authors (2):
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Bahaaldine Azarmi Bahaaldine Azarmi
Author Profile Icon Bahaaldine Azarmi
Bahaaldine Azarmi
Rich Collier Rich Collier
Author Profile Icon Rich Collier
Rich Collier
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Toc

Table of Contents (12) Chapters Close

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 Other Books You May Enjoy

Data organization

Before we can effectively wrangle all of this underlying data, we need to smartly segment it, possibly enrich it, and leverage the contextual information contained within it. First, we will focus on segmentation and enrichment.

Effective data segmentation

Simply by virtue of collecting some types of data (system performance metrics, log files, and so on) from underlying servers/hosts, there is likely already a natural segmentation of the data by server/host. Let's look at a sample measurement from Metricbeat:

{  
"_index":"metricbeat-6.0.0-2018.01.01",
"_type":"doc",
"_id":"ZQtas2ABB_sNnq-vMrgR",
"_score":1,
"_source...
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