Train Machine Learning Models Better, Cheaper, and Faster Using Amazon SagMaker Debugger

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Published Feb 20, 2025

It's time to stop manually tracking training metrics and switch to automatic alerts when model or system inefficiencies are detected. Amazon SageMaker Debugger makes it easy to optimize machine learning (ML) models by capturing training metrics in real-time such as loss, accuracy, and gradients and sending alerts when anomalies are detected. In this tech talk, we will show you how this works through an end-to-end example. We'll collect tensor data, utilize built-in rules to detect problems within a model and within system resources, and then automate remediation of issues. We will explore visualizations so you can see how easy it is to track metrics compared to manual techniques.

Learning Objectives:
*Learn how to use Amazon SageMaker Debugger to easily collect training data
*Learn how to use Amazon SageMaker Debugger’s automated actioning feature to terminate poorly performing training jobs and configure automated alerts
*Learn how to use the automated visualizations provided by Amazon SageMaker Debugger to identify resource bottlenecks and evaluate model performance

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/sagemaker/debugger/ Subscribe to AWS Online Tech Talks On AWS:
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