Collier Rich - Machine Learning with the Elastic Stack: Expert techniques to integrate machine learning with distributed search and analytics

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To the incredibly smart and talented development engineers of the Elastic Machine Learning team thanks for making an incredible product that artfully balances complexity with simplicity.

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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.

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.

Dan Noble is an accomplished full-stack web developer, data engineer, entrepreneur, and author with more than 12 years of industry experience and a passion for building novel software solutions that solve meaningful problems. Dan is the founder of Geofable, a software company that helps people tell stories with spatial data. He enjoys working with a variety of programming languages and tools, particularly Python, JavaScript, React, Elasticsearch, and Postgres.

Dan has been a user and advocate of Elasticsearch since 2011. He is the author of the book Monitoring Elasticsearch, and was a technical reviewer for several other books, including The Elasticsearch Cookbook, by Alberto Paro, and Learning Elasticsearch, by Abhishek Andhavarapu.

MatiasCascallares is a software engineer with more than 15 years of experience in software development in a variety of roles, with a deep focus on open source technologies and highly scalable environments. Having lived on three different continents, he has a wealth of experience in multicultural and distributed teams.

Nowadays, in the position of principal solutions architect at Elastic, he helps organizations to get value from their data and find success using the Elastic Stack. He has been involved in projects across multiple verticals, including finance and banking, transportation, e-commerce, and telecommunications.

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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.