Book Image

Machine Learning with the Elastic Stack - Second Edition

By : Rich Collier, Camilla Montonen, Bahaaldine Azarmi
5 (1)
Book Image

Machine Learning with the Elastic Stack - Second Edition

5 (1)
By: Rich Collier, Camilla Montonen, Bahaaldine Azarmi

Overview of this book

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 for 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 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 in your distributed search and data analysis platform.
Table of Contents (19 chapters)
1
Section 1 – Getting Started with Machine Learning with Elastic Stack
4
Section 2 – Time Series Analysis – Anomaly Detection and Forecasting
11
Section 3 – Data Frame Analysis

Overcoming the historical challenges in IT

IT application support specialists and application architects have a demanding job with high expectations. Not only are they tasked with moving new and innovative projects into place for the business, but they also have to keep currently deployed applications up and running as smoothly as possible. Today's applications are significantly more complicated than ever before—they are highly componentized, distributed, and possibly virtualized/containerized. They could be developed using Agile, or by an outsourced team. Plus, they are most likely constantly changing. Some DevOps teams claim they can typically make more than 100 changes per day to a live production system. Trying to understand a modern application's health and behavior is like a mechanic trying to inspect an automobile while it is moving.

IT security operations analysts have similar struggles in keeping up with day-to-day operations, but they obviously have a different focus of keeping the enterprise secure and mitigating emerging threats. Hackers, malware, and rogue insiders have become so ubiquitous and sophisticated that the prevailing wisdom is that it is no longer a question of whether an organization will be compromised—it's more of a question of when they will find out about it. Clearly, knowing about a compromise as early as possible (before too much damage is done) is preferable to learning about it for the first time from law enforcement or the evening news.

So, how can they be helped? Is the crux of the problem that application experts and security analysts lack access to data to help them do their job effectively? Actually, in most cases, it is the exact opposite. Many IT organizations are drowning in data.