ML Model Card Review Lab

Goal

Reinforce Model documentation, Evaluation, Bias review, Approval workflow through a small, inspectable activity. This is a learning exercise, not a production design: use a dedicated sandbox or test account, record what you observe, and do not copy the permissions or data-handling choices into production without review.

What you should be able to explain afterward: what AWS managed for you, what you configured, what evidence proves the result, and what could continue to cost money or expose data if you leave it behind.

Concepts Reinforced

• Model documentation
• Evaluation
• Bias review
• Approval workflow

Prerequisites

• A hypothetical or existing model scenario

Before you start

1. Choose one AWS Region and write it down; many resources and console views are Region-specific.
2. Confirm that you are operating only in an authorized non-production account and that you can identify the account ID before creating anything.
3. Open the AWS service documentation linked below if a console label or command option is unfamiliar. The lab is successful when you understand the observation, not when you click through it quickly.

Estimated Cost

Exact Steps

Read the entire sequence before beginning. Substitute your own test names for every placeholder, keep the Region consistent, and pause after each step to inspect the result. If a command returns an error, do not repeatedly retry it without reading the error: check Region, account, permissions, resource identifiers, and whether the preceding step actually completed.

1. Write intended use, forbidden use, training data source, evaluation metric, and risk statement.
2. Add known limitations and monitoring expectations.
3. Define who can approve production deployment.
4. List which artifacts must be retained for audit.

{
  "intendedUse": "Prioritize support tickets for review",
  "notFor": "Automated denial of customer service",
  "primaryMetric": "macroF1",
  "reviewRequiredBeforeProduction": true
}

Verification

Verification is the evidence that the concept worked. Capture the relevant ARN, status, identity, log entry, policy result, object version, or query output before cleanup. A successful command alone is not proof that the intended control behaved correctly.

• The model card explains what the model should not be used for.
• Metrics match the business risk.
• Approval is separated from model authoring.

If the result is different

• Confirm the selected Region and AWS account.
• Check the exact resource identifier and current status.
• Review the service event history, CloudTrail event, CloudWatch log, or command output when available.
• Re-read the relevant IAM policy, trust policy, security rule, or service setting instead of assuming the service is at fault.

Cleanup Steps

Cleanup is part of the lab. Remove test resources in dependency order, delete temporary credentials or local files, and check the billing or resource console for anything that remains. Some services retain versions, snapshots, logs, or recovery artifacts even after the visible parent resource is deleted.

• No cleanup for design-only path. Delete any temporary SageMaker artifacts if created.

Sources and Review Metadata