OP here: I ran 580 model-dataset experiments to show that, even if you try very hard, it is almost impossible to know that a model is degrading just by looking

OP here: I ran 580 model-dataset experiments to show that, even if you try very hard, it is almost impossible to know that a model is degrading just by looking at data drift results. "In my opinion, data drift detection methods are very useful when we want to understand what went wrong with a model, but they are not the right tools to know how my model's performance is doing. Essentially, using data drift as a proxy for performance monitoring is not a great idea. I wanted to prove that by giving data drift methods a second chance and trying to get the most out of them. I built a technique that relies on drift signals to estimate model performance and compared its results against the current SoTA performance estimation methods (PAPE and CBPE) to see which technique performs best."