This course is a complete hands-on guide to building an enterprise-grade AI Governance Command Center using Python, Streamlit, dashboards, evaluation pipelines, monitoring workflows, compliance mapping, and audit reporting.
As organizations adopt Generative AI, LLMs, AI agents, RAG applications, copilots, automation workflows, and third-party AI tools, they need more than policies and presentations. They need practical systems that can track what AI is being used, who is using it, how risky it is, how well it performs, whether it follows internal policies, and whether it can produce evidence for leaders, auditors, and regulators.
In this course, you will build a complete AI governance platform from scratch. You will start by creating an AI inventory that tracks models, agents, use cases, workflows, datasets, prompts, vendors, owners, reviewers, and approvers. Then you will build dashboards for AI usage visibility, including model call counts, team usage, top use cases, token consumption, estimated cost, expensive outliers, and usage trends across departments and business units.
