Amazon AWS Certified Machine Learning – Specialty: How to Effectively Address Model Drift for a Global Product Launch Using Amazon SageMaker?

Learn the most effective approach to address model drift issues when launching a global product using an Amazon SageMaker machine learning model trained on limited regional data.

Question

A telecommunications company has deployed a machine learning model using Amazon SageMaker. The model identifies customers who are likely to cancel their contract when calling customer service. These customers are then directed to a specialist service team. The model has been trained on historical data from multiple years relating to customer contracts and customer service interactions in a single geographic region.

The company is planning to launch a new global product that will use this model. Management is concerned that the model might incorrectly direct a large number of calls from customers in regions without historical data to the specialist service team.

Which approach would MOST effectively address this issue?

A. 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.
B. 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.
C. 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.
D. 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.

Answer

The most effective approach to address the issue of potentially incorrectly directing a large number of customer calls from regions without historical data to the specialist service team would be:

A. 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.

Explanation

Here’s why this is the best approach:

1. Amazon SageMaker Model Monitor is specifically designed to detect model drift by comparing live data captured from the model endpoint against a baseline dataset. This makes it ideal for identifying when the model’s performance degrades on data from new regions.
2. Creating a monitoring baseline on the original training dataset provides a reference point to measure drift against. Scheduling regular monitoring jobs allows proactively detecting drift.
3. Using Amazon CloudWatch alerts will promptly notify the data science team when regional data starts deviating significantly from the baseline, indicating the model is not performing well for those new regions.
4. Upon receiving the alert, data scientists can investigate by reevaluating the training dataset, expanding it with a larger, more representative data source that includes the new regions, and retraining the model on this enhanced dataset. This will improve the model’s ability to correctly handle calls from the new regions.

In contrast, the other options have some shortcomings:

• Option B uses SageMaker Debugger, but that is more suited for debugging model training issues rather than detecting drift in a production endpoint.
• Option C relies on manually analyzing calls routed to the specialist team, which is reactive rather than proactive, and could miss issues with false negatives.
• Option D uses CloudWatch Logs which are not as suitable as Model Monitor for detecting drift and lack built-in baselining capabilities.

Therefore, enabling SageMaker Model Monitor drift detection against a baseline, alerting with CloudWatch, and retraining on an expanded dataset is the most effective approach to handle this model drift problem when launching the product globally.

Amazon AWS Certified Machine Learning – Specialty certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Amazon AWS Certified Machine Learning – Specialty exam and earn Amazon AWS Certified Machine Learning – Specialty certification.