From the course: Machine Learning with SageMaker by Pearson
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Learning objectives - Amazon SageMaker Tutorial
From the course: Machine Learning with SageMaker by Pearson
Learning objectives
Welcome to Lesson 5, where we focus on evaluating and improving machine learning models. This lesson begins with understanding key evaluation metrics like accuracy, precision, and recall, essential for assessing model performance. We'll then explore StageMaker Clarify, a powerful tool for detecting bias and increasing model interpretability. Next, we'll dive into comparing models through A-B testing, helping you make data-driven and deployment decisions. Finally, we'll look at managing model versions using SageMaker Model Registry, enabling efficient tracking, approval and deployment workflows.
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Contents
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Learning objectives39s
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Model evaluation metrics: Accuracy, precision, and recall9m 39s
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Using SageMaker Clarify for bias detection and interpretability7m 40s
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Comparing model performance using A/B testing5m 47s
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Model A/B testing demonstration6m 26s
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Managing model versions with SageMaker Model Registry5m 55s
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Model registry demonstration10m 48s
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