While fine-tuning a deep learning model in SageMaker Studio, you expect issues such as vanishing gradients, underutilized GPU, and overfitting. You need realtime, comprehensive metrics during training and automatic reactions (predefined actions) when these problems are detected, with minimal operational overhead. Which option meets these needs?

SageMaker Debugger is the most suitable option because it is designed specifically for detecting common deep learning issues like vanishing gradients, underutilized GPUs, and overfitting during training. It offers built-in rules that automatically monitor these conditions in real-time and can be configured to trigger predefined actions (e.g., stopping the training job, sending notifications) when issues are detected, all with minimal operational overhead. TensorBoard is a visualization tool and doesn't inherently provide real-time automated detection and reaction capabilities. While it can show metrics, integrating it with SNS and Lambda for automated reactions adds significant operational complexity. CloudWatch default metrics offer general insights but typically lack the granular, deep learning-specific metrics (like gradients at each step) needed to detect issues like vanishing gradients directly. Extending CloudWatch metrics to include gradients would require custom code and significant effort, increasing operational overhead compared to Debugger's built-in functionality.