How would you design an automated retraining pipeline with drift detection in Azure ML?

An automated retraining pipeline uses monitoring systems to detect drift, triggers Azure ML pipelines, retrains models, validates performance, and redeploys models automatically.

Machine learning systems degrade over time due to data drift, concept drift, and changing environments. Automated retraining ensures models stay accurate without manual intervention.

Architecture components:

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