Hands-On Certified AI Governance Engineering with Python

Build dashboards, test models, evaluate agents, monitor risks, and create a complete AI Governance Command Center

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Build a complete AI Governance Command Center using Python, Streamlit, SQLite, dashboards, analytics, and exportable reports.
Create an enterprise AI inventory covering models, agents, copilots, workflows, use cases, datasets, prompts, tools, and approved vendors.
Track AI usage across users, teams, applications, models, regions, business units, token consumption, and estimated cost.
Build risk scoring engines that assess AI systems based on data sensitivity, autonomy, user impact, regulatory scope, and business risk.
Evaluate traditional ML models and LLM applications using accuracy, latency, error rates, groundedness, hallucination signals, safety checks, and response quali
Monitor AI agents by logging plans, tool calls, actions, retries, failures, approvals, escalations, overrides, and human-in-the-loop decisions.
Build RAG governance dashboards that measure retrieval quality, document freshness, source provenance, citation quality, and sensitive-data exposure risks.
Implement AI guardrails to detect PII, confidential data, prompt injection, jailbreak attempts, unsafe outputs, and prohibited actions.
Map AI systems to governance frameworks, regulations, internal policies, controls, evidence, exceptions, and compliance gaps.
Create model cards, AI impact assessments, approval workflows, audit trails, incident trackers, remediation workflows, and executive governance reports.

Basic familiarity with Python is recommended, including variables, functions, lists, dictionaries, loops, and reading CSV or JSON files.
Students should be comfortable installing Python packages and running simple Python scripts from a terminal or code editor.
A laptop or desktop computer capable of running Python, Streamlit, and a local database such as SQLite is required.
No previous AI governance, compliance, risk, legal, or audit experience is required; the course explains these concepts from the ground up.
No advanced mathematics, machine learning research background, or enterprise governance experience is required.
Familiarity with generative AI, LLMs, agents, RAG, or APIs is helpful but not required.
Students should be willing to work with sample datasets, logs, model outputs, policy rules, and dashboard data throughout the course.
A code editor such as VS Code and a modern web browser are recommended.

Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!

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