You are perhaps talking about some simple plumbing bugs. There are other kinds:

Why didn't the training converge

Validation/test errors are great but why is performance in the wild so poor

Why is the model converging so soon

Why is this all zero

Why is this NaN

Model performance is not great, do I need to move to something more complicated or am I doing something wrong

Did the nature of the upstream data change ?

Sometimes this feature is missing, how should I deal with this

The training set and the data on which the model will be deployed are different. How to address this problem

The labelers labelled only the instances that are easy to label, not chosen uniformly from the data. How to train with such skewed label selection

I need to update model but with a few thousand data points but not train from scratch. How do I do it

Model too large which doubles can I replace with float32

So on and so forth. Many times models are given up on prematurely because the expertise to investigate lackluster performance does not exist in the team.