For ETL, write unit tests to handle some input edge cases. E.g Null values, mis-formatting, values out of range as well as some simple working cases.

For model training, the test focus is on having "valid" hyperparams and configurations. I write test cases to try to overfit on a small training set. i.e Confirm the model learns. There are also some robustness tests that I sometimes run post training, but those are very specific to certain NLP tasks, applications.

For model serving, successful parsing of the request and subsequent feature transformation (if any), very similar to ETL.