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Datasets and pre-trained models come with intrinsic biases. Most methods rely on spotting them by analyzing misclassified samples in a semi-automated human computer validation. Deep neural networks, typically fine-tuned foundational models, are widely used in sectors like healthcare, finance, and criminal justice, where biased predictions can have serious societal impacts. These models often function as black boxes, lacking transparency and interpretability, which can obscure potential biases introduced during fine-tuning. Such biases arise from datasets and can lead to harmful outcomes by reinforcing existing inequalities. Recent methods for addressing biases in subpopulation shift scenarios primarily analyze validation data without investigating the
Ever spent hours converting design mockups into pixel-perfect mobile UI code? I’ve been there. But here’s the game-changer: Large Language Models (LLMs) can now transform your UI designs into…
In this post, we present a framework to customize the use of Amazon SageMaker Model Monitor for handling multi-payload inference requests for near real-time inference scenarios. SageMaker Model Monitor monitors the quality of SageMaker ML models in production. Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling.