Topic 1 Question 4
Case Study - A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring. The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3. The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application. Which action will meet this requirement?
Configure the application to invoke an AWS Lambda function that runs a SageMaker Clarify job.
Invoke an AWS Lambda function to pull the sagemaker-model-monitor-analyzer built-in SageMaker image.
Use AWS Glue Data Quality to monitor bias.
Use SageMaker notebooks to compare the bias.
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コメント(4)
- 正解だと思う選択肢: A
A. Yes, Clarify allows to get bias - https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-configure-processing-jobs.html B. No, the built-in image sagemaker-model-monitor-analyzer provides a range of model monitoring capabilities (constraint suggestion, statistics generation, constraint validation against a baseline, and emitting Amazon CloudWatch metrics) but you need Clarify for bias C. No, Glue Data Quality doesn't analyze bias D. No, well from a Notebook you can execute pretty much everything including a Clarify Job, however notebooks are for experiments and models development not for enabling real-time application features
👍 3ninomfr642024/12/21 - 正解だと思う選択肢: A👍 2GiorgioGss2024/11/27
- 正解だと思う選択肢: A
SageMaker Clarify is a tool designed to detect and monitor bias in datasets and models. It provides built-in capabilities for bias analysis, both pre-training (data bias) and post-training (model bias). Using AWS Lambda to invoke the job ensures automation and on-demand execution, reducing operational complexity while meeting the requirement for monitoring bias drift.
👍 2tigrex732024/11/27
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