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.
