Q3ML Solution Monitoring, Maintenance, and Security
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 must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints. Which solution will meet this requirement?
← → navigate · a answer
Community votes
Discussion · 7
D 6
Selected Answer: D
This tricked me because option D isn't worded clearly:
A. No, SageMaker Experiments lets you track and organize your experiments, but not approve models
B. No, SageMaker ML Lineage Tracking allows you to track model lineage but does not let you approve a model
C. No, SageMaker Model Monitor lets you monitor data quality, model quality, bias, and feature attribution
D. Yes, after you create a model version, you typically evaluate its performance and then update the approval status of the model version. You can update the approval status of a model version by using the SDK, SageMaker Studio console, or with a condition step in a SageMaker AI pipeline
D 4
Selected Answer: D
https://docs.aws.amazon.com/en_us/sagemaker/latest/dg/model-registry-approve.html
"You can update a model version's approval status by using the AWS SDK "
D 4
Selected Answer: D
The SageMaker Model Registry inside the pipeline provides functionality to manually or programmatically approve models for production deployment.
D 2
Selected Answer: D
SageMaker Pipelines is built to orchestrate machine learning workflows, including manual approval steps for model registration. You can define a step in the pipeline where a manual approval process is needed before the model's status is changed to "Approved" for deployment.
D 2
Selected Answer: D
The SageMaker Model Registry in the pipeline provides functionality to manually or programmatically approve models for production deployment.
D 1
Selected Answer: D
The SageMaker Model Registry in the pipeline provides functionality to manually or programmatically approve models for production deployment
D 1
the model monitor approves model to be deployed, when it then integrates with with the sagemaker pipeline its provides a workflow.