Reactive Machine Learning Systems

Sample Chapter

By Jeff Smith

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.

The free sample PDF available here includes chapter 1. All registrants qualify for a Lightbend discount and will save 40% off the price of Reactive Machine Learning Systems (all formats) as it becomes available by referencing promotional code lbsmith. If you are interested in purchasing the full book get it here. Offer only valid at manning.com.

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About Lightbend

Lightbend (Twitter: @Lightbend) is dedicated to helping developers build Reactive applications on the JVM. With the Lightbend Reactive Platform, developers can create message-driven applications that scale on multicore and cloud computing architectures by using projects like Lagom, Play Framework, Akka, Scala, Java, and Apache Spark. To help our customers succeed, Lightbend partners with technology pioneers such as Databricks, IBM, and Mesosphere.