The space of possible inputs to a machine learning model is essentially infinite. This makes it a completely different ballpark than unit testing code, where a

The space of possible inputs to a machine learning model is essentially infinite. This makes it a completely different ballpark than unit testing code, where a small set of tests can cover good amount of interesting cases, and where various coverage metrics can reasonably be used to estimate completeness. It is also hard to generate tests for ML models due to lack of good oracles (detectors of wrong results). Defense against adversarial inputs is still considered an open research problem, I believe.