Machine Learning with the Elastic Stack

Key Features

• Get actionable insights from your Elasticsearch data with the help of this handy guide
• Sift through the large volumes of data and combine the power of machine learning with the search and analytics capabilities of the Elastic stack
• Get a significant performance and operational advantage by integrating your Elastic stack with external data science tools

Book Description

The open source log-analysis stack now has machine learning components for more sophisticated analytics, albeit through a commercial add-on.

The book will start with understanding how to install and set up the Xpack package, you will see how you can perform time-series analysis on varied kinds of data such as log files, network flows, application metrics and financial data. You will learn how to deploy machine learning within the Elastic Stack for logging, security and metrics. Moving on, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster, and made resilient to failure. You will also see how you can integrate different third-party data science tools with the Elastic stack to get the most efficient insights from your data. Finally, you will also understand the performance aspects of incorporating machine learning within the Elastic ecosystem, and see how you can create anomaly detection jobs and view results right from Kibana.

What you will learn

• Install Elastic stack to use Elastic ML
• Learn how Elastic ML has been used to detect critical business anomalies.
• Create jobs to reveal anomalies
• Explore security analytics with Elastic ML
• Understand multi-dimension analysis and profile entities
• Use Elastic ML result to do forensic analysis in Elastic Graph