Book Image

Scala for Machine Learning

By : Patrick R. Nicolas
Book Image

Scala for Machine Learning

By: Patrick R. Nicolas

Overview of this book

Table of Contents (20 chapters)
Scala for Machine Learning
About the Author
About the Reviewers


This completes the introduction of the most common scalable frameworks built using Scala. It is quite challenging to describe frameworks, such as Akka and Spark, as well as new computing models such as Actors, futures, and RDDs, in a few pages. This chapter should be regarded as an invitation to further explore the capabilities of those frameworks in both a single host and a large deployment environment.

In this last chapter, we learned:

  • The benefits of asynchronous concurrency

  • The essentials of the actor model and composing futures with blocking or callback modes

  • How to implement a simple Akka cluster to squeeze performance of distributed applications

  • The ease and blazing performance of Spark's resilient distributed datasets and the in-memory persistency approach