AWS ML Summit 2021 | Gain better insights while training ML models with SageMaker Debugger

As state-of-the-art machine learning (ML) models grow in size and complexity, debugging becomes increasingly difficult. Just as unit tests boost traditional software development, an automated ML debugging library can save time and money. Amazon SageMaker Debugger is an ML feature that automatically identifies and stops underperforming training jobs. It automatically captures relevant data during training, such as model tensors, system resource utilization, and metrics about training operations. SageMaker Debugger provides rules that automatically identify issues related to model quality and computational performance. In this session, learn how you can profile and debug you training runs and how to set up real-time alerts.

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Where we can sample notebook like demo you were just showed?