Machine Learning with the Elastic Stack

Expert techniques to integrate machine learning with distributed search and analytics

Rich Collier , Bahaaldine Azarmi 지음

Packt(GCO Science) 2019년 02월 14일 출간

작품소개

책소개

▶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

▶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

▶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.

목차

▶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

책 속으로

▶What this book covers
？ Chapter 1, Machine Learning for IT, is an introductory and background primer on the historical challenges of manual data analysis in IT and security operations. This chapter provides a comprehensive overview of the theory of operation of Elastic ML in order to get an intrinsic understanding of what is happening under the hood.

？ Chapter 2, Installing the Elastic Stack with Machine Learning, walks you through the comprehensive and descriptive installation procedures for Elasticsearch, Kibana, Metricbeat, and the enabling of the ML feature. This is followed by several working examples of data analysis executed on Metricbeat data to introduce the basics of the mechanics of the ML analysis jobs.

？ Chapter 3, Event Change Detection, goes into detail regarding the count-based analysis techniques that are at the crux of effective log file analysis.

？ Chapter 4, IT Operational Analytics and Root Cause Analysis, explains how leveraging Elastic ML 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 5, Security Analytics with Elastic Machine Learning, explains how anomaly detection and behavioral analytics have become a must-have feature for assisting security experts in detecting and unraveling the advanced persistent threats posed by today's cyber adversaries. Elastic ML's approach of detecting behavioral outliers fits perfectly into the strategies of those analysts who use the Elastic Stack for security-based machine data.

？ Chapter 6, Alerting on ML Analysis, explains the different techniques for integrating the proactive notification capability of Elastic Alerting with the insights uncovered by ML in order to make anomaly detection even more actionable.

？ Chapter 7, Using Elastic ML Data in Kibana Dashboards, explains how to augment your traditional Kibana dashboard visualizations with information gleaned from ML.

？ Chapter 8, Using Elastic ML with Kibana Canvas, covers how to create pixel-perfect live reports with real-time data analysis from ML.

？ Chapter 9, Forecasting, explains how Elastic ML'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 10, ML Tips and Tricks, 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.

출판사 서평

▶ Preface
Data analysis, manual charting, thresholding, and alerting have been an inherent part of IT and security operations for decades. Until the advent of sophisticated machine learning algorithms and techniques, much of the burden of proactive insight, problem detection, and root cause analysis fell onto the shoulders of the analysts. As the complexity and scale of modern applications and infrastructure has grown exponentially, it is apparent that humans need help. Elastic machine learning (ML) is an effective, easy-to-use solution for anomaly detection and forecasting use cases in relation to time-series machine data. This definitive elastic ML guide will get the reader proficient in the operation and techniques of advanced analytics without the need to be well-versed in data science.

작가정보

저자(글) Rich Collier

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.

저자(글) Bahaaldine Azarmi

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.