Which solution will meet these requirements?
Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyperparameters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team. Retrain the model by using only data from the previous several months.
Use only data from the previous several months to perform incremental training to update the model. Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
Explanations:
Performing incremental training updates the model with new data, which is essential for improving accuracy. Activating Amazon SageMaker Model Monitor allows for the detection of performance issues and can send notifications, thereby addressing both requirements effectively.
While Amazon SageMaker Model Governance can help manage models, it does not automatically adjust model hyperparameters based on performance issues. This option does not specifically address monitoring model performance or sending notifications effectively.
Amazon SageMaker Debugger is used for debugging and profiling training jobs but does not inherently monitor deployed models for performance issues. Additionally, retraining with only the previous several months of data may not be sufficient for improving model accuracy, as it may lead to model drift and loss of valuable historical data.
Although using data from the previous several months for incremental training can help update the model, it does not explicitly address the ongoing monitoring of model performance or sending notifications about issues, making it less comprehensive than Option A.
I chart that the answer is:
Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.