Model debugging time from days to minutes (Hebrew)

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Interested in learning how to debug model characteristics during training?

Training ML models is a multi-step, complex task that is iterative and time-consuming. During training, ML models learn patterns in the training data to make accurate predictions. This learning occurs through multiple iterations of the data and adjusting the parameter values for each iteration. It is challenging to ensure that a model is progressively learning the correct values of the different parameters. Additionally, it is not easy to analyze and debug model characteristics without building additional tools, making the entire process cumbersome.


Amazon SageMaker Debugger makes it much easier to easily analyze and debug model characteristics during training, using the Amazon SageMaker Studio visual interface. When anomalies are detected, SageMaker Debugger sends alerts for developers’ to take remedial actions, reducing the time it takes to debug models from days to minutes. The debug data remains in the customer’s AWS account, allowing SageMaker Debugger to be used for most privacy-sensitive applications.


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AWS Developers
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Amazon Web Services, AWS, AWS Online Tech Talks
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