A real estate price prediction model deployed on a website has gradually lost accuracy. The engineer needs to improve the model and be alerted automatically about future performance degradations. Which approach meets both needs?

The correct approach addresses both model improvement and future performance monitoring. Incremental training is a suitable method to update a model that has gradually lost accuracy, as it allows the model to learn from new data without retraining from scratch. Amazon SageMaker Model Monitor is specifically designed to detect data drift and model quality issues in deployed models and can send notifications, fulfilling the requirement for automatic alerts on performance degradation. Incorrect options: Amazon SageMaker Model Governance is a broader concept for managing ML lifecycles, not a direct tool for automatic hyperparameter adjustment or performance monitoring in this context. Amazon SageMaker Debugger is primarily for analyzing training jobs to identify issues like vanishing gradients or overfitting, not for monitoring deployed model performance. While retraining with recent data is a valid strategy, incremental training is often more efficient for gradual degradation. The key missing piece in this option is a robust, continuous monitoring solution like Model Monitor to proactively detect future issues.