As machine learning (ML) becomes increasingly ubiquitous across many industries and applications, it is also becoming difficult to understand the rationale behind the results of ML models. Understanding how ML models arrive at their outcomes and how to consistently predict results from ML models is known as interpretability. This session explores the science behind interpretability and shows how to use Amazon SageMaker to understand the workings of ML models and their predictions. Learn the interpretation of deep learning algorithms with an introduction to model explainability, a key area of interest within ML.

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