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

Product typeBook
Published inMay 2021
Reading LevelBeginner
PublisherPackt
ISBN-139781801070034
Edition2nd Edition
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Authors (3):
Rich Collier
Rich Collier
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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
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Camilla Montonen

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

Bahaaldine Azarmi
Bahaaldine Azarmi
author image
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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Anomaly detection in the Logs app

The Logs app inside of the Observability section of Kibana offers a similar view of your data as the Discover app. However, the users who appreciate more of a live tail view of their logs, regardless of the index the data is stored, will love the Logs app:

Figure 8.16 – The Logs app, part of the Observability section of Kibana

Notice that there is both an Anomalies tab and a Categories tab. Let's first discuss the Categories section.

Log categories

Elastic ML's categorization capabilities, first shown back in Chapter 3, Anomaly Detection, are applied in a generic way to any index of unstructured log data. Within the Logs app, however, categorization is employed with some more strict constraints on the data. In short, the data is expected to be in Elastic Common Schema (ECS) with certain fields defined (especially a field called event.dataset).

Note

The logs dataset from Chapter 7, AIOps and Root...

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Machine Learning with the Elastic Stack - Second Edition
Published in: May 2021Publisher: PacktISBN-13: 9781801070034

Authors (3)

author image
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

author image
Camilla Montonen

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

author image
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