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Machine Learning with the Elastic Stack - Second Edition

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

Product type Book
Published in May 2021
Publisher Packt
ISBN-13 9781801070034
Pages 450 pages
Edition 2nd Edition
Languages
Concepts
Authors (3):
Rich Collier Rich Collier
Profile icon Rich Collier
LinkedIn
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.
See other products by Rich Collier
Camilla Montonen Camilla Montonen
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Bahaaldine Azarmi Bahaaldine Azarmi
Profile icon Bahaaldine Azarmi
LinkedIn
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.
See other products by Bahaaldine Azarmi
View More author details

Table of Contents (19) Chapters

Preface Section 1 – Getting Started with Machine Learning with Elastic Stack
Chapter 1: Machine Learning for IT Chapter 2: Enabling and Operationalization Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
Chapter 3: Anomaly Detection Chapter 4: Forecasting Chapter 5: Interpreting Results Chapter 6: Alerting on ML Analysis Chapter 7: AIOps and Root Cause Analysis Chapter 8: Anomaly Detection in Other Elastic Stack Apps Section 3 – Data Frame Analysis
Chapter 9: Introducing Data Frame Analytics Chapter 10: Outlier Detection Chapter 11: Classification Analysis Chapter 12: Regression Chapter 13: Inference Other Books You May Enjoy Appendix: Anomaly Detection Tips

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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Authors (3)

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Profile icon Rich Collier
LinkedIn
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.
See other products by Rich Collier
Profile icon Camilla Montonen
LinkedIn
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
See other products by Camilla Montonen
Profile icon Bahaaldine Azarmi
LinkedIn
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.
See other products by Bahaaldine Azarmi
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