Preface
Elastic Stack, previously known as the ELK Stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as classification, regression, and outlier detection.
The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within the Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with.
By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning into your distributed search and data analysis platform.
Who this book is for
If you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.
What this book covers
Chapter 1, Machine Learning for IT, acts as an introductory and background primer on the historical challenges of manual data analysis in IT and security operations. This chapter also provides a comprehensive overview of the theory of operation of Elastic machine learning in order to get an intrinsic understanding of what is happening under the hood.
Chapter 2, Enabling and Operationalization, explains enabling the capabilities of machine learning in the Elastic Stack, and also details the theory of operation of the Elastic machine learning algorithms. Additionally, a detailed explanation of the logistical operation of Elastic machine learning is explained.
Chapter 3, Anomaly Detection, goes into detail regarding the unsupervised automated anomaly detection techniques that are at the heart of time series analysis.
Chapter 4, Forecasting, explains how Elastic machine learning's sophisticated time series models can be used for more than just anomaly detection. Forecasting capabilities enable users to extrapolate trends and behaviors into the future so as to assist with use cases such as capacity planning.
Chapter 5, Interpreting Results, explains how to fully understand the results of anomaly detection and forecasting and use them to your advantage in visualizations, dashboards, and infographics.
Chapter 6, Alerting on ML Analysis, explains the different techniques for integrating the proactive notification capability of Elastic alerting with the insights uncovered by machine learning in order to make anomaly detection even more actionable.
Chapter 7, AIOps and Root Cause Analysis, explains how leveraging Elastic machine learning to holistically inspect and analyze data from disparate data sources into correlated views gives the analyst a leg up in terms of legacy approaches.
Chapter 8, Anomaly Detection in other Elastic Stack Apps, explains how anomaly detection is leveraged by other apps within the Elastic Stack to bring added value to data analysis.
Chapter 9, Introducing Data Frame Analysis, covers the concepts of data frame analytics, how it is different from time series anomaly detection, and what tools are available to the user to load, prepare, transform, and analyze data with Elastic machine learning.
Chapter 10, Outlier Detection covers the outlier detection analysis capabilities of data frame analytics along with Elastic machine learning.
Chapter 11, Classification Analysis, covers the classification analysis capabilities of data frame analytics along with Elastic machine learning.
Chapter 12, Regression covers the regression analysis capabilities of data frame analytics along with Elastic machine learning.
Chapter 13, Inference, covers the usage of trained machine learning models for "inference" – to actually predict output values in an operationalized manner.
Appendix: Anomaly Detection Tips, includes a variety of practical advice topics that didn't quite fit in other chapters. These useful tidbits will help you to get the most out of Elastic ML.
To get the most out of this book
You will need a system with a good internet connection and an Elastic account.
Download the example code files
You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Machine-Learning-with-Elastic-Stack-Second-Edition. In case there's an update to the code, it will be updated on the existing GitHub repository.
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Download the color images
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Conventions used
There are a number of text conventions used throughout this book.
Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "The analysis can also be split along categorical fields by setting partition_field_name."
A block of code is set as follows:
18/05/2020 15:16:00 DB Not Updated [Master] Table
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
export DATABRICKS_AAD_TOKEN=<azure-ad-token>
Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Let's now click the View results button to investigate in detail what the anomaly detection job has found in the data."
Tips or important notes
Appear like this.
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