Smith Jeff. Reactive Machine Learning Systems

Manning Publications, 2018. — 275 p. — ISBN: 9781617293337. — MEAP version 11Manning Early Access Program (MEAP).
MEAP began February 2016. Publication in February 2018 (estimated).Reactive Machine Learning Systems teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. This example-rich guide starts with an overview of machine learning systems while focusing on where reactive design fits. Then you'll discover how to develop design patterns to implement and coordinate ML subsystems. Using Scala and powerful frameworks such as Spark, MLlib, and Akka, you'll learn to quickly and reliably move from a single machine to a massive cluster. Finally, you'll see how you can operate a large-scale machine learning system over time. By the end, you'll be employing the principles of reactive systems design to build machine learning applications that are responsive, resilient, and elastic.
"Machine Learning and Akka are complex topics and can be dry. This book makes the topics an easy sweep and very practical. By far one of the best I have read."~ Shobha Iyer"An great introduction to a large and complex topic - made all the more engaging thanks to the witty and engaging writing style."~ Jason Hales"This is an engaging text for those who want to understand how a reactive systems philosophy can be applied to the creation, implementation, and optimization of machine learning applications."~ Anonymous ReviewerWhat's insideFunctional programming for distributed systems
Reactive techniques like futures, actors, and supervision
Spark and MLlib, and Akka
Scala-based examples
Predictive microservices
Data models for uncertain data
Design patterns for machine learning systems