Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
5 (1)
Book Image

Machine Learning with the Elastic Stack - Second Edition

5 (1)
By: Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview of this book

Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform.
Table of Contents (19 chapters)
Section 1 – Getting Started with Machine Learning with Elastic Stack
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
Section 3 – Data Frame Analysis

Understanding influencers in split versus non-split jobs

You might question whether or not it is necessary to split the analysis by a field, or merely hope that the use of influencers will give the desired effect of identifying the offending entity.

Let's remind ourselves of the difference between the purpose of influencers and the purpose of splitting a job. An entity is identified by an anomaly detection job as an influencer if it has contributed significantly to the existence of the anomaly. This notion of deciding influential entities is completely independent of whether or not the job is split. An entity can be deemed influential on an anomaly only if an anomaly happens in the first place. If there is no anomaly detected, there is no need to figure out whether there is an influencer. However, the job may or may not find that something is anomalous, depending on whether or not the job is split into multiple time series. When splitting the job, you are modeling (creating...