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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

ML job throughput considerations

ML is awesome, and is no doubt very fast and scalable, but there will still be a practical upper bound of events/second processed to any ML job, depending on a couple of different factors:

  • The speed at which data can be delivered to the ML algorithms (that is, query performance)
  • The speed at which the ML algorithms can chew through the data, given the desired analysis

For the latter, much of the performance is based upon the following:

  • The function(s) chosen for the analysis, that is, count is faster than lat_long
  • The chosen bucket_span (longer bucket spans are faster than smaller bucket spans because more buckets analyzed per unit of time compound the per-bucket processing overhead that's writing results and so on)

However, if you have a defined analysis setup and can't really change it for other reasons, then there's not really...

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