Machine Learning with the Elastic Stack: Expert techniques to integrate machine learning with distributed search and analytics - Softcover

Rich Collier; Bahaaldine Azarmi

Softcover

ISBN 10: 1788477545 ISBN 13: 9781788477543

Publisher: Packt Publishing, 2018

Synopsis

Leverage Elastic Stack's machine learning features to gain valuable insight from your data

Key Features

• 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

Book Description

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.

What you will learn

• 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

Who this book is for

If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

Table of Contents

1. Machine Learning for IT
2. Installing the Elastic Stack with Machine Learning
3. Event Change Detection
4. IT Operational Analytics and Root Cause Analysis
5. Security Analytics with Elastic Machine Learning
6. Alerting on ML Analysis
7. Using Elastic ML data in Kibana dashboards
8. Using Elastic ML with Kibana Canvas
9. Forecasting
10. ML Tips and Tricks

About this title

About the Author

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.

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

Edition details

• Publisher: Packt Publishing
• Publication date: 2018
• Language: English
• ISBN 10: 1788477545
• ISBN 13: 9781788477543
• Binding: Paperback
• Number of pages: 304