Automate Machine Learning Models from debugging deep learning to detecting model drift in production

This tutorial will explain how state-of-the-art algorithms built into Amazon SageMaker are used to detect declines in machine learning (ML) model quality. One of the big factors that can affect the accuracy of models is the difference in the data used to generate predictions and what was used for training. For example, changing economic conditions could drive new interest rates affecting home purchasing predictions. Amazon SageMaker Model Monitor automatically detects drift in deployed models and provides detailed alerts that help you identify the source of the problem so you can be more confident in your ML applications.

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