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

Using anomaly detection on runtime fields

In some cases, it might be necessary to analyze the value of a field that doesn't exist in the index mappings but can be calculated dynamically from other field values. This capability to dynamically define field values has existed for quite some time in Elasticsearch as script fields, but starting in v7.11, script fields are replaced by an updated concept known as runtime fields. In short, runtime fields are treated like first-class citizens in the Elasticsearch mapping (if defined there) and will eventually allow the user to promote a runtime field into an indexed field.

Users can define runtime fields in the mapping or only in the search request. It is good to note that at the time of writing, there is no support for definitions of runtime fields in the data feed of an anomaly detection job. However, if the runtime fields are defined in the mappings, then the anomaly detection job can leverage them seamlessly.


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