How do you design an automated retraining pipeline that responds to model drift? What triggers should initiate retraining, and what safeguards prevent deploying degraded models?

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Automated retraining pipelines maintain model performance over time by detecting when models degrade and triggering updates without manual intervention. Designing effective automation requires thoughtful trigger mechanisms and robust safeguards.