AWS Certified Cloud Practitioner

Explanation

SageMaker Model Monitor is the correct choice because:

1. Purpose-built for model monitoring: SageMaker Model Monitor is specifically designed to detect model drift and data quality issues in production environments.

2. Data drift detection: It automatically detects when incoming data deviates from the training data distribution, which is exactly what the manufacturer needs.

3. Alerting capabilities: Model Monitor can send alerts when drift is detected, allowing teams to take corrective actions.

4. Real-time monitoring: It monitors models in real-time or batch mode, providing continuous visibility into model performance.

Why other options are incorrect:

• SageMaker Canvas (B): This is a no-code tool for building ML models, not for monitoring production models.

• SageMaker Neo (C): This is a compiler that optimizes ML models for different hardware platforms, not for monitoring drift.

• SageMaker Ground Truth (D): This is a data labeling service for creating training datasets, not for monitoring production models.

Key AWS Service: Amazon SageMaker Model Monitor helps maintain model quality in production by detecting concept drift, data drift, and quality issues, ensuring models continue to perform accurately over time.