assert test_performance > threshold ? This would only be a sanity check for big deviations though. For more subtle changes one may need to do a proper statistic

assert test_performance > threshold ? This would only be a sanity check for big deviations though. For more subtle changes one may need to do a proper statistical test. I believe the jury is still out of how to do that properly for deep ML models - challenge is the lack of independence in CV folds and generally the compute time it takes to evaluate. However, I would probably not do performance check inside a unit-testing framework. Instead treat this as quality indicators like performance benchmarks, code coverage etc. It may be a "gate", that needs to pass to allow a new model into production. To evaluate performance over time, one would preferably want labeled datasets for test gathered at different points in time. Which requires a (reliable) continuous labeling process. One can also gather customer feedback about performance, track those as metrics. These things are probably more in the "monitoring" part of a system, rather than unit-testing time though.