The pipeline in Spark ML was inspired by scikit-learn in Python, which is referenced here for completeness:
http://scikit-learn.org/stable/
ML pipelines make it easy to combine multiple algorithms used to implement a production task in Spark. It would be unusual to see a use case in a real-life situation that is made of a single algorithm. Often a number of cooperating ML algorithms work together to achieve a complex use case. For example, in LDA-based systems (for example, news briefings) or human emotion detection, there are a number of steps before and after the core system to be implemented as a single pipe to produce any meaningful and production-worthy system. See the following link for a real-life use case requiring ...
