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)

Job groups

We saw in Chapter 4, IT Operational Analytics and Root Cause Analysis, that there were good reasons to simultaneously view the results of several jobs against different kinds of data. Therefore, it makes sense that sometimes we need the ability to logically group jobs together around a common theme. To accomplish this, let's review the Job groups feature, which was introduced to ML in version 6.1.

Job groups allow the user to arbitrarily tag jobs with keywords for organizational purposes. You can, for example, determine that all jobs that are related to a specific application should be tagged with the application name. You can assign a job to a group at creation time, or you can edit the job after its creation; the process is simple. For example, the first time we assign a job to a Job group, the name will not be recognized and will create a new group name:

Subsequently...