How to Become a AI Compliance Automation Specialist

Build a GitHub Actions workflow that automatically evaluates a scikit-learn model against fairness metrics (demographic parity, equalized odds, calibration) using fairlearn on every push, and blocks deployment if thresholds are exceeded. Generate a compliance report artifact.

Create an Open Policy Agent (OPA) policy set in Rego that evaluates model metadata (risk tier, fairness scores, documentation completeness, data lineage status) against deployment criteria. Build a Python service that queries OPA before allowing model promotion in an MLflow registry.

Build a system that automatically generates comprehensive model cards from MLflow experiment metadata, fairlearn audit results, Great Expectations data validation reports, and training data lineage information. Output in HuggingFace Model Card format.

Develop a questionnaire-driven and metadata-driven risk classification system that maps AI use cases to EU AI Act risk tiers. Integrates with a model registry to auto-classify based on use case, data sensitivity, affected population, and decision criticality.

Build a production monitoring system using Evidently AI that periodically evaluates deployed models for bias drift, data drift, and performance degradation. Create a Streamlit dashboard showing compliance status across all monitored models with alerting via Slack/PagerDuty.

Design an automated adversarial testing pipeline for large language models that generates compliance-relevant test prompts (discriminatory outputs, hallucinated facts, privacy leaks, harmful content), evaluates responses using safety classifiers, and produces a compliance risk report with categorization by regulatory article.