Answer: SageMaker Model Monitor

## 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.

Author: Ritesh Yadav