Answer: Amazon SageMaker Model Monitor

## Detailed Explanation ### Understanding the Requirement The question describes a company that needs to **identify changes in original model quality** for **multiple ML models** in production, with the goal of **resolving issues** when quality degrades. This is a classic production ML monitoring scenario where models can experience performance degradation over time due to various factors. ### Analysis of AWS SageMaker Options **Amazon SageMaker Model Monitor (Option D)** is specifically designed for this exact use case. It provides: 1. **Model Quality Monitoring**: Continuously tracks model performance metrics (accuracy, precision, recall, etc.) against established baselines 2. **Drift Detection**: Identifies both data drift (changes in input data distribution) and model drift (changes in model predictions) 3. **Multi-Model Support**: Can monitor multiple models simultaneously, which aligns with the requirement of "multiple ML models" 4. **Automated Alerts**: Integrates with Amazon CloudWatch to trigger notifications when performance thresholds are breached 5. **Issue Remediation Support**: Provides detailed reports and insights that help teams understand what's causing quality degradation, facilitating root cause analysis and resolution ### Why Other Options Are Less Suitable - **Amazon SageMaker JumpStart (Option A)**: Primarily a solution for getting started with ML models through pre-built solutions and notebooks. It's focused on model development and deployment, not production monitoring. - **Amazon SageMaker HyperPod (Option B)**: Designed for distributed training of large foundation models across clusters of GPUs. This is a training infrastructure solution, not a monitoring service. - **Amazon SageMaker Data Wrangler (Option C)**: A data preparation and feature engineering tool that helps clean, transform, and prepare data for ML. While important for model development, it doesn't address production monitoring needs. ### Key Differentiators Model Monitor stands out because it: - **Operates in production environments** rather than development phases - **Compares current performance against historical baselines** to detect meaningful changes - **Provides actionable insights** for remediation, not just detection - **Supports the complete monitoring lifecycle** from baseline creation to ongoing monitoring and alerting This service directly addresses the core requirement of identifying quality changes in production models to enable timely issue resolution.

Author: LeetQuiz Editorial Team