What’s the benefit of using a feature pipeline instead of inline transformations?

Example Concept: A feature pipeline automates the sequence of data preprocessing steps, such as normalization, encoding, and feature selection, into a single workflow. This approach allows data scientists to encapsulate the entire transformation process, ensuring that the same operations are consistently applied to both training and test datasets. By using a pipeline, you can easily modify or extend preprocessing steps without altering the core logic of your model, facilitating better version control and easier debugging.