Book Image

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
Book Image

Machine Learning with the Elastic Stack

By: Rich Collier, Bahaaldine Azarmi

Overview of this book

Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly.
Table of Contents (12 chapters)

Ignoring time periods

Often, people ask how they can get ML to ignore that a certain time period has occurred. Perhaps it was an expected maintenance window, or perhaps something was broken within the data ingest pipeline and data was lost for a few moments. There are a few ways that you can get ML to ignore time periods and, for distinction, we'll separate them into two groups:

  • A known, upcoming window of time
  • An unexpected window of time that is discovered after the fact

To illustrate things, we'll use a reference job and dataset that has an anomaly on the date of February 9th:

Ignoring an upcoming (known) window of time

Two methods can be used to ignore an upcoming window of time, as shown in the following...