A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML…

The requirement is to create an on-demand workflow to monitor bias drift for a deployed model. Amazon SageMaker Clarify is the specific service designed to detect bias in models and data, both before training and after deployment (bias drift). A SageMaker Clarify job can be run as a processing job. To create an "on-demand workflow" that can be triggered by a web application, an AWS Lambda function is the ideal serverless compute service. The Lambda function can be invoked via an API call from the application, and it will, in turn, initiate the SageMaker Clarify processing job to perform the bias drift analysis. This architecture directly and precisely meets all stated requirements.

B: This is imprecise. A Lambda function would start a SageMaker job that uses the image; it does not "pull" the image itself. More importantly, it names a generic monitor container, whereas SageMaker Clarify is the specific tool for bias. C: AWS Glue Data Quality is used to monitor the quality of data within data pipelines and data lakes (e.g., null counts, uniqueness), not for detecting statistical bias in machine learning models. D: SageMaker notebooks are for interactive development and experimentation. They are not a suitable component for an automated, on-demand production workflow triggered by an application.

1. Amazon SageMaker Clarify Documentation: "After a model is deployed

you can use SageMaker Clarify to detect bias drift and feature attribution drift... You can run a SageMaker Clarify job on demand by using the Amazon SageMaker Python SDK

or you can set up a scheduled job with SageMaker Model Monitor." This confirms Clarify is the correct tool and can be run on-demand.

Source: AWS Documentation

"Detect Post-Training Bias Drift with Amazon SageMaker Clarify".

2. Amazon SageMaker Model Monitor Documentation: "SageMaker Clarify is integrated with SageMaker Model Monitor to help you monitor your models for bias drift over time... You can start a baselining job to generate constraints and then set up a scheduled monitoring job. You can also run a single (on-demand) monitoring job." This shows the integration and the on-demand capability.

Source: AWS Documentation

"Bias Drift".

3. AWS Architecture for MLOps: Architectural patterns for MLOps frequently use AWS Lambda functions to orchestrate various components of the ML lifecycle

including triggering SageMaker Processing Jobs

in response to events or API calls. This validates the use of Lambda as the trigger for the on-demand workflow.

Source: AWS Whitepaper

"MLOps: Continuous delivery for machine learning on AWS". (See architectural diagrams showing Lambda functions orchestrating SageMaker jobs).