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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 looked at how we can use Canvas to leverage the output of ML to build a customized report so that information can appeal to any audience. With the power of the full flexibility of Elasticsearch (and even the use of Elasticsearch SQL queries), we can make real-time data-driven infographics where your only limit is your creativity.

In the next chapter, Chapter 9, Forecasting, we will delve into the world of forecasting, where we can have ML extrapolate data trends into the future to satisfy a whole new set of use cases.

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