Q4Deployment and Orchestration of ML Workflows
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?
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A 11
Selected Answer: A
A. Yes, Clarify lets you 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 set 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 run pretty much everything including a Clarify Job, however notebooks are for experiments and models development not for enabling real-time application features
A 3
Selected Answer: A
https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html
A 3
Selected Answer: A
SageMaker Clarify is a tool built to detect and monitor bias in datasets and models. It offers built-in capabilities for bias analysis, both pre-training (data bias) and post-training (model bias). Using AWS Lambda to invoke the job keeps it automated and on-demand, lowering operational complexity while meeting the requirement for monitoring bias drift.
A 2
Selected Answer: A
SageMaker Clarify can be used to analyze bias drift in models. By integrating this with a Lambda function, the workflow can be triggered on-demand whenever the application needs bias monitoring.
A 2
Selected Answer: A
SageMaker Clarify can be used to analyze bias drift in models. By integrating this with a Lambda function, the workflow can be triggered on-demand whenever the application needs bias monitoring.
A 2
Selected Answer: A
A looks like the best answer
A 1
Selected Answer: A
SageMaker Clarify can be used to analyze bias drift in models. By integrating this with a Lambda function, the workflow can be triggered on-demand whenever the application needs bias monitoring