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  • Book Overview & Buying Machine Learning with the Elastic Stack
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Machine Learning with the Elastic Stack

Machine Learning with the Elastic Stack

By : Rich Collier, Bahaaldine Azarmi
4.8 (5)
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Machine Learning with the Elastic Stack

Machine Learning with the Elastic Stack

4.8 (5)
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)
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Summary

In this chapter, we used different visualization capabilities of Kibana to render a dataset and leverage anomaly detection results to either annotate and correlate. This helps analysts who want to see anomaly detection results within the dashboard framework that they are familiar with and use every day.

In the next chapter, Chapter 8, Using Elastic ML with Kibana Canvas, we'll focus on users that don't necessarily want to go through a dashboard and need an infographic style of experience. This is where we'll introduce Canvas, a PowerPoint-like experience in Kibana that's connected to live data stored in Elasticsearch.

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Machine Learning with the Elastic Stack
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