Yeah, debugging ML assignments can get really frustrating when errors pop up out of nowhere. In my experience, a lot of the headaches come from subtle data preprocessing issues or model config mismatches that aren’t obvious at first glance.
One thing that’s saved me tons of time is breaking the pipeline into smaller testable chunks early on – like validating inputs right after loading, then again post-feature engineering. I’ve also found that bringing in solid machine learning development solutions for the trickier model architecture parts helps catch those weird bugs before they spiral.