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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

Holistic application visibility

IT operations and IT security organizations are collecting massive amounts of data. Some of that data is collected and/or stored in specialized tools, but some may be collected in general-purpose data platforms such as the Elastic Stack. But the question still remains: what percentage of that data is being paid attention to? By this, we mean the percentage of collected data that is actively inspected by humans, or being watched by some type of automated means (defined alarms based on rules, thresholds, and so on). Even generous estimates might put the percentage in the range of single digits. So, with 90% or more data being collected going unwatched, what's being missed? The proper answer might be that we don't actually know.

Before we admonish IT organizations for the sin of collecting piles of data, but not watching it, we need to understand...

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