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)
1
Section 1 – Getting Started with Machine Learning with Elastic Stack
4
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
11
Section 3 – Data Frame Analysis

Elastic ML job types

When we start using the Elastic ML UI to configure anomaly detection jobs, we will see that there are five different job wizards that are shown:

Figure 3.1 – The Create job UI showing different configuration wizards

The existence of these different configuration wizards implies that there are different "types" of jobs. In actuality, there is really only one job type—it is just that the anomaly detection job has many options, and many of these wizards make certain aspects of that configuration easier. Everything that you may desire to configure can be done via the Advanced wizard (or the API). In fact, when Elastic ML was first released as beta in v5.4, that was all that existed. Since then, the other wizards have been added for simplicity and usability in specific use cases.

An anomaly detection job has many configuration settings, but the two most important ones are the analysis configuration and the datafeed...