MLS-C01 Question 265
Select 3You are a Machine Learning Engineer working on a model deployment pipeline in AWS. Your team wants to implement an error monitoring solution to identify issues such as data drift, performance degradation, and inference errors in real-time for a SageMaker endpoint. Which combination of AWS services and features should you use to build this solution?
- A
Amazon SageMaker Model Monitor
- B
AWS CloudWatch Metrics and Logs
- C
Amazon SageMaker Feature Store
- D
Amazon SNS (Simple Notification Service)
- E
AWS Glue for Data Integration
Show answer and explanation
Correct answers: A, B, D
Explanation
To build an effective error monitoring solution for a SageMaker endpoint, you need to use Amazon SageMaker Model Monitor to detect data drift, bias, and quality issues. AWS CloudWatch Metrics and Logs can track endpoint performance metrics and errors. Amazon SNS facilitates real-time notifications to alert stakeholders about detected issues. Together, these services provide a comprehensive monitoring solution. Feature Store and Glue, while useful for other purposes, do not contribute to error monitoring in this context.
- A. Correct.
Amazon SageMaker Model Monitor provides capabilities to detect data quality issues, model bias, and feature drift in real-time. This is essential for error monitoring in deployed models.
- B. Correct.
AWS CloudWatch Metrics and Logs allow you to track metrics such as latency, error rates, and other logs generated by the SageMaker endpoint, which are critical for monitoring performance.
- C. Incorrect.
Amazon SageMaker Feature Store is used for managing and serving features to models but does not directly support error monitoring or detection of data drift or inference issues.
- D. Correct.
Amazon SNS can be configured to send notifications or alerts when anomalies or errors are detected by Model Monitor or CloudWatch, enabling a complete monitoring solution.
- E. Incorrect.
AWS Glue is primarily used for data integration and ETL processes, but it is not relevant for real-time error monitoring in this scenario.