Chapter 5: Scalable Machine Learning with PySpark

In the previous chapters, we have established that modern-day data is growing at a rapid rate, with a volume, velocity, and veracity not possible for traditional systems to keep pace with. Thus, we learned about distributed computing to keep up with the ever-increasing data processing needs and saw practical examples of ingesting, cleansing, and integrating data to bring it to a level that is conducive to business analytics using the power and ease of use of Apache Spark's unified data analytics platform. This chapter, and the chapters that follow, will explore the data science and machine learning (ML) aspects of data analytics.

Today, the computer science disciplines of AI and ML have made a massive comeback and are pervasive. Businesses everywhere need to leverage these techniques to remain competitive, expand their customer base, introduce novel product lines, and stay profitable. However, traditional ML and data science...