Feature-based pipelines
Feature-based pipelines do not have specific classes because they are much lower level. They are the model.fit() and model.predict() statements from the standard Python ML implementation. These pipelines require software developers to prepare the data manually and also to take care of the results manually; that is, by implementing preprocessing steps such as converting data to tables using one-hot encoding and post-processing steps such as converting the data into a human-readable output.
An example of this kind of pipeline was the prediction of defects that we have seen in the previous parts of the book; therefore, they do not need to be repeated.
What is important, however, is that all pipelines are the way that link the ML domain with the software engineering domain. The first activity that I do after developing a pipeline is to test it.
