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Explain the concept of model drift in the context of AutoML. Discuss how AutoML tools monitor and address model drift to maintain the performance of deployed models. Provide examples of techniques used for detecting and mitigating model drift.
A
Performance monitoring, data drift detection, and model retraining.
B
Feature engineering, hyperparameter tuning, and ensemble methods.
C
Data augmentation, transfer learning, and adversarial training.
D
Model compression, quantization, and pruning.
