Distribution shifts occur when the statistical patterns of training data differ from real-world data, undermining model accuracy. Data scientists and ML engineers detect them to retrain systems, ensuring reliable predictions in healthcare, finance, and autonomous driving. This safeguards performance as environments evolve.
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