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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

Machine Learning

Authors (3):

Rich Collier

Camilla Montonen

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

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Appendix: Anomaly Detection Tips

Anomaly detection in Elastic APM

Elastic APM takes application monitoring and performance management to a whole new level by allowing users to instrument their application code to get deep insights into the performance of individual microservices and transactions. In complex environments, this could generate a large number of measurements and poses a potentially paradoxical situation – one in which greater observability is obtained via this detailed level of measurement while possibly overwhelming the analyst who has to sift through the results for actionable insights.

Fortunately, Elastic APM and Elastic ML are a match made in heaven. Anomaly detection not only automatically adapts to the unique performance characteristics of each transaction type via unsupervised machine learning, but it can also scale to handle the possibly voluminous amounts of data that APM can generate.

While the user is always free to create anomaly detection jobs against any kind of time-series...

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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.

See other products by Rich Collier

Camilla Montonen

Camilla Montonen is a Senior Machine Learning Engineer at Elastic.

See other products by 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.

See other products by Bahaaldine Azarmi

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