Summary
This concludes the first of the two chapters dedicated to scalability in using Scala to build machine learning-based applications.
This first part dealt with the parallel collections in Scala as a trivial but effective way to make your application scalable. You also learned about the benefits of asynchronous concurrency in Scala, the Akka framework, and the essential elements of the Actor model and composed futures as applied to improve the performance of a distributed application.
The Akka framework is the underlying mechanism used in Apache Spark to distribute runtime execution and exchange/broadcast data. The Apache Spark framework is the topic of the next chapter.