[D] Data drift is not a good indicator of model performance degradation

The world is a dynamic mess. So, it is natural for things to change. And, data drift methods are good tools for detecting those changes.

But in the context of ML monitoring, data drift methods are often presented as the go-to solution for detecting performance degradation in ML models. However, these methods often fool us.


In our findings, data drift doesn't always imply a decline in the model's performance. There could be several reasons for this:

* The drifted feature …

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