Why current testing processes in AI/ML are not enough?
The current notions of quality assurance and testing in AI/ML pipelines is based on the idea of validation using a random set-aside set of data on which the model is tested and metrics computed thereof. Metrics like accuracy on the random set-aside data set termed ambiguously as test data, is the usual rubric for evaluation …
Why current testing processes in AI/ML are not enough? Read More »