Machine Learning with the Elastic Stack

More Information
  • Install the Elastic Stack to use machine learning features
  • Understand how Elastic machine learning is used to detect a variety of anomaly types
  • Apply effective anomaly detection to IT operations and security analytics
  • Leverage the output of Elastic machine learning in custom views, dashboards, and proactive alerting
  • Combine your created jobs to correlate anomalies of different layers of infrastructure
  • Learn various tips and tricks to get the most out of Elastic machine learning

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.

  • Combine machine learning with the analytic capabilities of Elastic Stack
  • Analyze large volumes of search data and gain actionable insight from them
  • Use external analytical tools with your Elastic Stack to improve its performance
Page Count 304
Course Length 9 hours 7 minutes
ISBN 9781788477543
Date Of Publication 31 Jan 2019


Bahaaldine Azarmi

Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.

Rich Collier

Rich Collier is a solutions architect at Elastic. Joining the Elastic team from the Prelert acquisition, Rich has over 20 years' experience as a solutions architect and pre-sales systems engineer for software, hardware, and service-based solutions. Rich's technical specialties include big data analytics, machine learning, anomaly detection, threat detection, security operations, application performance management, web applications, and contact center technologies. Rich is based in Boston, Massachusetts.