Data Engineering

On the data ops site, they might be serving different ML models with different drift detection needs. So how do they respond to something where they might not detect a drift, but maybe there is one particular ML model that says, yeah, this is totally unacceptable distribution of data. Does that cause problems for either of the models or maybe the data engineering team? Who deals with this sort of like disparity and statistical distribution needs of the underlying data? Again, it comes back to the question of whether the data stream is drifting, whether it's a natural drift of the data.