Debugging ML vs Normal Applications: An Intro

If you’re used to debugging traditional software, ML debugging will feel strange.

In normal software, poor performance usually means there’s a bug. You find it, fix it, ship it. The process is familiar: check the logs, reproduce the error, step through the code.

ML doesn’t work that way. Your code can be completely correct and your model can still fail. Poor performance might mean your features don’t contain useful signals, your hyperparameters are wrong, your data has quality issues, or your architecture isn’t suited to the problem. You’re not debugging code—you’re debugging data, features, model architecture, and training dynamics all at once.

This makes debugging harder in two ways. First, the search space is much larger. Second, the feedback loop is slower. Testing a fix in traditional software takes seconds. Retraining an ML model can take hours or days.

The failure modes are different too. ML systems don’t crash with helpful stack traces. They fail quietly. A model can overfit to noise or learn spurious correlations, and you won’t know until you look at the metrics.

The best approach I’ve learned is to be disciplined about development. Start with the simplest possible model: one or two features, the most basic architecture. Get it running end-to-end before adding complexity. Then change one thing at a time. Add a feature. Adjust a hyperparameter. Check whether metrics improve. If they don’t, stop and debug before moving forward.

You’re not debugging a program. You’re debugging a system where code, data, and configuration interact in non-obvious ways.

Track everything. Log every experiment, every metric, every configuration change. When something breaks, you need to know exactly what changed.

The pattern I see repeatedly: people skip the simple baseline, build something complex, and spend weeks debugging a system they don’t understand. Start simple. Change one thing at a time. Debug less.