The most underused testing technique in ML: synthetic adversarial examples.

Most engineers test their model on a held-out test set and call it a day. But the test set comes from the same distribution as the training set. It doesn't test what happens when reality looks different.

What I do: generate synthetic examples designed to break the model. For NLP: add typos, use slang, mix languages, use double negatives, make the input ambiguous. For CV: add noise, change lighting, rotate awkwardly, partially occlude objects. For tabular: inject outliers, use extreme values, leave fields blank.

These synthetic adversarial tests have caught more production bugs than any traditional test set. Because production users are creative in ways your training data isn't.

A customer once broke our sentiment model by writing entirely in sarcasm. "Oh GREAT, another update that totally works perfectly." Our model scored it positive. A synthetic sarcasm test set would have caught this immediately.

Building a synthetic adversarial test suite takes maybe two days. It becomes a permanent asset — every model you build gets tested against it.

Think of it as stress testing for ML. You don't deploy a web server without load testing. Why would you deploy a model without adversarial testing?