It's not as straightforward as Kaggle representing one stage in a pipeline. Kaggle also fixes this stage in an unnatural way.

In real life, there is a feedback loop from modelling, to data processing. In Kaggle all you can do is improve your model. You can't go back and change how data was processed, or collect different sorts of data.

Also, some flows simply don't fit into Kaggle's train/test paradigm. E.g. suppose you have an online algorithm that continuously updates parameters. Or situations where the model is used to generate the train/test data.

Finally some situations may have stringent computational requirements, e.g. <100ms to classify a single instance.