Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your data
Key Features:
- Integrate machine learning with distributed search and analytics
- Preprocess and analyze large volumes of search data effortlessly
- Operationalize machine learning in a scalable, production-worthy way
Book Description:
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.
What You Will Learn:
- Find out how to enable the ML commercial feature in the Elastic Stack
- Understand how Elastic machine learning is used to detect different types of anomalies and make predictions
- Apply effective anomaly detection to IT operations, security analytics, and other use cases
- Utilize the results of Elastic ML in custom views, dashboards, and proactive alerting
- Train and deploy supervised machine learning models for real-time inference
- Discover various tips and tricks to get the most out of Elastic machine learning
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.
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.
Camilla Montonen is a Senior Machine Learning Engineer at Elastic.
Bahaaldine Azarmi, or Baha for short, is the head of solutions architecture in the EMEA South region at Elastic. Prior to this position, Baha co-founded ReachFive, a marketing data platform focused on user behavior and social analytics. He has also worked for a number of different software vendors, including Talend and Oracle, where he held positions as a solutions architect and architect. Prior to Machine Learning with the Elastic Stack, Baha authored books including Learning Kibana 5.0, Scalable Big Data Architecture, and Talend for Big Data. He is based in Paris and holds an MSc in computer science from Polytech'Paris.
