AI Evaluation, Safety & Responsible AI
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Curated Certification CAI07
Make AI systems measurable, trustworthy and safer through evaluation, red teaming, guardrails, fairness and governance.
Benchmark: Curated source set: Google Skills + Microsoft Learn + DeepLearning.AI + Arize AI + DeepLearning.AI + Giskard + DeepLearning.AI + GuardrailsAI + DeepLearning.AI + AMD/Meta -- Credential note: this is a Zuyini mastery certificate built from the cited public courses. It is not issued, sponsored or endorsed by the source institutions, providers or companies.
Core Course Stack
1. Introduction to Responsible AI
2. Responsible AI for Developers: Fairness & Bias
3. GitHub responsible AI
4. Operationalize AI responsibly with Azure AI Foundry
5. Evaluating AI Agents
6. Red Teaming LLM Applications
7. Safe and Reliable AI via Guardrails
8. Improving Accuracy of LLM Applications
Hands-On Mastery Capstone
Threat-model and evaluate one LLM/agent application. Build quality and safety evals, run a red-team exercise, add guardrails, test fairness or harmful-output scenarios, and produce a residual-risk report.
Proof Rubric
- repository / notebook / artifact
- evaluation evidence
- short technical or executive readout
- reflection