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)

The orchestration

ML sequences all of these pieces together when an ML job is configured to run. A simplified version of this process is shown in the following diagram:

Simplified sequence of ML's procedures per bucket_span

In general, the preceding procedures are done once per bucket_span—however, additional optimizations are done to minimize I/O. Those details are beyond the scope of this book. The key takeaway, however, is that this orchestration enables ML to be online (that is, not offline/batch) and constantly learning on newly ingested data. This process is also automatically handled by ML so that the user doesn't have to worry about the complex logistics required to make it all happen.