What is the purpose of a pipeline in Spark ML?

The purpose of a pipeline in Spark ML is to organize transformers and estimators to streamline the process of building a machine learning model. Pipelines allow for the systematic chaining of several data processing steps, which can include transformations (such as scaling, encoding, and feature extraction) and model training (estimators). This structured workflow helps in simplifying the model training phase, enhancing code readability, and ensuring that the same sequence of data processing is applied consistently during both training and inference.

By consolidating these steps into a cohesive structure, a pipeline enables users to manage and encapsulate the entire machine learning process from data preparation to model evaluation in a single unit. This makes it easier to test and optimize models, as well as to deploy them more effectively into production environments.

Other options typically do not relate as directly to the core functionality of a pipeline in Spark ML. For instance, while visualizing data transformations is valuable, it is not a primary function of pipelines. Similarly, storing datasets during model training and validating models using external datasets, while essential aspects of the machine learning workflow, do not capture the essence of what a pipeline is designed to achieve in Spark ML.