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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

The results index

As we mentioned previously, ML analyzes data and summarizes the results into a results index. By default, this index is called .ml-anomalies-shared, unless the job was configured to use a dedicated index via ticking the box in the configuration user interface (UI) or via setting the results_index_name field when using the API. If that was the case, then the results index will be called .ml-anomalies-custom-myname, where myname is either the declared value of the results_index_name field when using the API, or the name of the job itself when using the UI. In either case, the results index can be quite small compared to the size of data that was used to feed the ML analysis. In addition, an index alias is also created of the .ml-anomalies-jobname form, where jobname is the name of the job. The definition of this alias can be seen by hitting the following API call...

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