Explain your ML models with Amazon SageMaker Clarify

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Published Feb 20, 2025

In today’s world where predictions are made by machine learning (ML) algorithms at scale, it’s increasingly important for tech organizations to be able to explain to their customers and internal teams why they made a certain decision based on an ML model’s prediction. Explaining ML models and understanding the reasoning behind the predictions is often difficult, but critical for responsible use of ML. In this webinar, learn how to identify the importance of various features in overall model predictions as well as for individual inferences using Shapley values and detect any shifts in feature importance over time after the model is deployed to production.

Learning Objectives:
* Objective 1: Understand which features contributed most to your model’s overall prediction-making process.
* Objective 2: Explain individual model predictions.
* Objective 3: Monitor your model for changes in behavior.

***To learn more about the services featured in this talk, please visit: https://aws.amazon.com/sagemaker/clarify Subscribe to AWS Online Tech Talks On AWS:
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