In this chapter, we studied the different machine learning steps and the common ML challenges that we face. We also covered the Spark ML algorithms and how they can be applied on large volumes of data, represented as resilient distributed datasets. This chapter also covered R as the most preferred programming tool for statisticians, data scientists, data analysts, and data architects. We learned about Mahout, which provides the usual machine learning algorithms with a highly scalable implementation, along with a case study on Spark.
In the next chapter, we will get an overview of Hadoop on the cloud.
