Phase 6: Capstone — Ship a Responsible AI Audit Pipeline · 50 min · Python · scikit-learn · Fairlearn

Running a Complete Audit — End-to-End on a Real Model

The audit is the artifact. The artifact is the evidence. The evidence is the compliance.

Hiring signal: Running a complete end-to-end audit and producing a professional compliance package is the exact deliverable RAI engineers produce in their first 90 days on the job.

What you will learn

Run the complete audit pipeline on a trained model
Interpret and consolidate results across all audit modules
Generate the final compliance package with all artifacts
Validate that the audit package satisfies NIST AI RMF, EU AI Act, and ISO 42001 requirements

The Problem

In Lesson 1, you assembled the toolkit. Now you run it on a real model with real bias and interpret the results. This is the exact workflow you'll execute in your first 90 days as a RAI engineer: train a model, run the audit, interpret the findings, and produce a compliance package.

The Audit Workflow

1. Prepare data and train model
2. Run bias audit → identify fairness failures
3. Run SHAP explainability → identify key drivers
4. Generate model card → document the model
5. Run governance assessment → classify risk tier
6. Build risk register → track identified risks
7. Generate transparency report → integrate everything
8. Validate compliance → check against frameworks

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers Running the Full Audit, What's Next — plus a hands-on lab, quiz, and project artifact.

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