What is model drift, and how do you detect and handle it in production? What monitoring strategies and retraining approaches address drift effectively?

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Model drift refers to the degradation of model performance over time due to changes in the underlying data or relationships. Production ML systems require continuous monitoring to detect drift and processes to address it before business impact becomes significant.