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General

Data Quality Monitoring in AWS SageMaker

First things first, what is data quality monitoring? Data quality monitoring for machine learning can generally be thought of from two perspectives. One perspective is that of traditional data-engineering. This type of monitoring is concerned with the “physical” characteristics of the data and ensuring they are what you expect them to be. It involves criteria […]

General

Boost AI Fairness and Explainability with Amazon SageMaker Clarify

From hiring decisions to loan approvals and even healthcare recommendations, machine learning (ML) impacts our lives daily. Fairness and explainability are crucial in this context. Fairness means data is balanced, and model predictions are fair across groups. Checking for fairness ensures that negative outcomes are fair across all groups, such as age or gender. Explainability […]