In this chapter, we learnt about the components that are inherent in a data-driven, automated machine learning system. We also outlined how a possible high-level architecture for such a system might look in a real-world situation. We also got an overview of MLlib-Spark's machine learning library-compared to other machine learning implementations from a performance perspective. In the end, we looked at new features in various versions of Spark starting from Spark 1.6 to Spark 2.0.
In next chapter, we shall discuss how to obtain publicly-available datasets for common machine learning tasks. We will also explore general concepts to process, clean, and transform data so that it can be used to train a machine learning model.