Which approach would MOST effectively address this issue?
Enable Amazon SageMaker Model Monitor data capture on the model endpoint. Create a monitoring baseline on the training dataset. Schedule monitoring jobs. Use Amazon CloudWatch to alert the data scientists when the numerical distance of regional customer data fails the baseline drift check. Reevaluate the training set with the larger data source and retrain the model.
Enable Amazon SageMaker Debugger on the model endpoint. Create a custom rule to measure the variance from the baseline training dataset. Use Amazon CloudWatch to alert the data scientists when the rule is invoked. Reevaluate the training set with the larger data source and retrain the model.
Capture all customer calls routed to the specialist service team in Amazon S3. Schedule a monitoring job to capture all the true positives and true negatives, correlate them to the training dataset, and calculate the accuracy. Use Amazon CloudWatch to alert the data scientists when the accuracy decreases. Reevaluate the training set with the additional data from the specialist service team and retrain the model.
Enable Amazon CloudWatch on the model endpoint. Capture metrics using Amazon CloudWatch Logs and send them to Amazon S3. Analyze the monitored results against the training data baseline. When the variance from the baseline exceeds the regional customer variance, reevaluate the training set and retrain the model.
Explanations:
This option enables monitoring of the model’s performance and identifies when the model encounters data from new regions that differ significantly from the training set. By setting up a monitoring baseline, it allows for detection of data drift, which is critical when deploying a model globally. If drift is detected, data scientists can be alerted to reevaluate and retrain the model with more representative data, thus addressing the risk of incorrect call routing.
While enabling Amazon SageMaker Debugger could help monitor the training process, it does not directly address the issue of post-deployment monitoring of the model’s predictions in a new region. Custom rules measure training variances but do not effectively manage model performance or alert based on incoming regional data, which is essential for this situation.
Capturing all customer calls to the specialist service team could provide useful data, but this option lacks a proactive approach to monitoring and does not immediately address potential issues arising from new regional data. The model’s performance could still deteriorate before corrective action is taken, as it relies on post-hoc analysis rather than ongoing monitoring.
Enabling CloudWatch to capture logs and metrics is beneficial, but simply analyzing these results against the training data baseline does not provide a mechanism to alert data scientists about regional variances in real-time. This approach is reactive rather than proactive, potentially leading to delays in identifying issues with model predictions in new regions.
As far as I’m aware, the answer is:
Enable Amazon SageMaker Model Monitor data capture on the model endpoint. Create a monitoring baseline on the training dataset. Schedule monitoring jobs. Use Amazon CloudWatch to alert the data scientists when the numerical distance of regional customer data fails the baseline drift check. Reevaluate the training set with the larger data source and retrain the model.