You're reading from Machine Learning with the Elastic Stack - Second Edition

Product typeBook

Published inMay 2021

Reading LevelBeginner

PublisherPackt

ISBN-139781801070034

Edition2nd Edition

Languages

Python

Tools

Elasticsearch

Concepts

Machine Learning

Authors (3):

Rich Collier

Camilla Montonen

Bahaaldine Azarmi

View More author details

Results index schema details

As we have already hinted, inside the results index, there are a variety of different documents, each with their own usefulness with respect to understanding the results of the anomaly detection jobs. The ones we will discuss in this section are the ones that directly relate to the three levels of abstraction that we discussed previously in this chapter. They are aptly named as follows:

result_type:bucket: To give bucket-level results
result_type:record: To give record-level results
result_type:influencer: To give influencer-level results

The distribution of these document types will depend on the ML job configuration and the characteristics of the dataset being analyzed. These document types are written with the following heuristic:

result_type:bucket: One document is written for every bucket span's worth of time. In other words, if the bucket span is 15 minutes, then there will be one document of this type being written...

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Machine Learning with the Elastic Stack - Second Edition

Published in: May 2021Publisher: PacktISBN-13: 9781801070034

Authors (3)

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.
Read more about Rich Collier

Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Read more about Camilla Montonen

Bahaaldine Azarmi

Bahaaldine Azarmi, Global VP Customer Engineering at Elastic, guides companies as they leverage data architecture, distributed systems, machine learning, and generative AI. He leads the customer engineering team, focusing on cloud consumption, and is passionate about sharing knowledge to build and inspire a community skilled in AI.
Read more about Bahaaldine Azarmi

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