ML model development is trivial

In my field, ML model development is quite straightforward and can be done in a day, given clean data. AutoML or basic manual pipeline is usually sufficient. There's no need of custom feature engineering or loss functions.

Thus, such ML model development is trivial and satisfies use cases. And such a process scales easily, given new data and targets.

How can one grow with skills in such a situation? Moreover, if one is interviewing, how can one claim any expertise …

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