Summary

In this chapter, we covered a large surface area of Spark: machine learning with its associated capabilities. We covered core model development and prediction along with feature extraction and model evaluation. Machine learning is a vast subject and requires a lot more study, experimentation, and practical experience with interesting data science problems. Two books that are relevant to Spark Machine Learning are Packt's own book Machine Learning with Spark, Nick Pentreath, and O'Reilly's Advanced Analytics with Spark, Sandy Ryza, Uri Laserson, Sean Owen, and Josh Wills. Both are excellent books that you can refer to. In the next chapter, we will look at another interesting topic: the processing graphs and graph algorithms using the GraphX APIs.