Introducing Amazon SageMaker Debugger – Get complete insights into the training process of machine learning models
Amazon SageMaker Debugger is a new capability of Amazon SageMaker that provides complete insights into the training process of machine learning (ML) models by automating the capture and analysis of data from training runs at real time, with no code changes.
Amazon SageMaker Debugger is a new capability of Amazon SageMaker that provides complete insights into the training process of machine learning (ML) models by automating the capture and analysis of data from training runs at real time, with no code changes.
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.
Amazon SageMaker Debugger is available today. To learn more, read the blog post here and refer to the documentation to get started.