Exam AWS Certified AI Practitioner AIF-C01 topic 1 question 43 discussion

Amazon SageMaker Clarify provides functionality to detect and identify potential bias in data both before and after training, helping teams uncover imbalances in datasets that might lead to biased model predictions. This is essential for ensuring fairness and compliance, especially in sensitive applications. Why Not the Other Options? A. Integrates a Retrieval Augmented Generation (RAG) workflow: RAG workflows are used for combining retrieved documents with model outputs, typically in language models, but this is not a function of SageMaker Clarify. B. Monitors the quality of ML models in production: Monitoring model quality in production is handled by SageMaker Model Monitor, not SageMaker Clarify. C. Documents critical details about ML models: This functionality is part of Amazon SageMaker Model Cards, which documents model details for transparency and compliance.

Explicação: O Amazon SageMaker Clarify é uma ferramenta que auxilia na detecção de vieses e na explicabilidade de modelos de machine learning (ML). Ele oferece funcionalidades como: Detecção de vieses: Ajuda a identificar possíveis vieses nos dados durante a preparação e também nos modelos após o treinamento. ​ Explicabilidade de modelos: Fornece insights sobre como os modelos de ML fazem previsões, aumentando a transparência

Identifies potential bias during data preparation: This is the core functionality of Amazon SageMaker Clarify. It helps data scientists and AI practitioners detect bias in training data, providing insights into how the data might introduce bi

Amazon Sagemake Clarify helps to Identify bias, how much models makes prediction, datasets or models reflections and more.

D. Identifies potential bias during data preparation   Amazon SageMaker Clarify is a tool designed to help understand, debug, and improve machine learning models. One of its key functionalities is to identify potential bias in datasets and models. It can analyze datasets for imbalances, fairness issues, and other biases that could impact the model's performance and fairness