Course Includes:
- Price: FREE
- Enrolled: 448 students
- Language: English
- Certificate: Yes
- Difficulty: Beginner
Coupon verified 10:02 AM (updated every 10 min)
Course Folder
What You'll Learn
- 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.
Requirements
- 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.
Description
This course contains the use of artificial intelligence.
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.
You will also build practical tools for AI risk management, including risk scoring by use case, model, workflow, autonomy level, data sensitivity, user impact, and regulatory exposure. You will create remediation workflows for open issues, assigned actions, overdue items, closure rates, and risk reduction tracking.
The course also covers model evaluation, LLM evaluation, and AI agent monitoring. You will learn how to track performance metrics such as latency, error rates, success rates, response quality, hallucination signals, unsafe-output indicators, and drift. You will build prompt and response logs for traceability, monitor agent tool calls and decisions, and measure human-in-the-loop review rates, escalations, overrides, and approval workflows.
A major part of the course focuses on AI compliance and policy enforcement. You will map AI systems to governance frameworks, internal policies, controls, evidence, exceptions, and compliance gaps. You will build policy versioning, rule tracking, guardrail effectiveness dashboards, sensitive data exposure detection, PII checks, prompt injection alerts, jailbreak detection, and unsafe-output monitoring.
You will also create practical governance artifacts such as model cards, AI impact assessments, audit trails, incident trackers, remediation reports, board summaries, audit packets, and downloadable compliance evidence.
By the end of the course, you will have built a portfolio-ready AI Governance Command Center that brings together inventory, usage, cost, risk, compliance, evaluations, guardrails, agent monitoring, incident tracking, audit evidence, and executive reporting in one unified dashboard.
This course is ideal for AI engineers, Python developers, data scientists, machine learning engineers, AI product managers, risk professionals, compliance teams, security teams, auditors, consultants, and technology leaders who want to move beyond AI governance theory and learn how to build real governance systems.
If you want to understand AI governance, responsible AI, AI risk management, LLM monitoring, agent governance, RAG governance, AI compliance, and AI dashboards through hands-on Python projects, this course is designed for you.
Who this course is for:
- Python developers who want to build practical AI governance
- risk
- compliance
- monitoring
- and dashboard applications.,AI engineers
- machine learning engineers
- data scientists
- and LLM developers who need to evaluate
- monitor
- and govern models
- agents
- and RAG systems.,AI product managers
- technical program managers
- and solution architects responsible for deploying AI safely at scale.,Risk
- compliance
- privacy
- security
- audit
- and governance professionals who want to understand how AI controls can be implemented through working software.,Enterprise architects and technology leaders who need visibility into AI inventory
- usage
- cost
- risk
- compliance
- and performance.,Consultants and professionals building AI governance services
- internal tools
- dashboards
- or proof-of-concept platforms for clients.,Students and career changers who want a portfolio-ready project demonstrating Python
- AI governance
- dashboards
- model evaluation
- agent monitoring
- and compliance workflows.,Anyone who wants to move beyond AI governance theory and build a complete
- practical governance platform from scratch.
Recommended Courses
AI Agent Development with Python
FREE
Learn to create AI assistants, research agents, tool-using agents, and multi-agent applications.
Enrolled
100 Days of AWS: From Cloud Basics to Real-World Mastery
FREE
A step-by-step journey through AWS fundamentals, security, DevOps, data, and capstone projects.
Enrolled
Enterprise Generative AI Systems on AWS Certification Course
FREE
Design, Secure, Scale, and Govern Production-Ready Generative AI, RAG, Agents, and Multimodal Systems on AWS
Enrolled
AI Foundations for Professionals
FREE
Build practical AI knowledge, use generative AI tools effectively, and make informed decisions in the modern workplace.
Enrolled
100 Web Development Projects in 100 Days
FREE
Build 100 independent projects from beginner to advanced and master HTML, CSS, JavaScript, APIs, and full-stack skills.
Enrolled
Claude AI for Business Automation: Build AI Workflow & Agent
FREE
Learn to automate business processes, create AI agents, build Claude-powered workflows, and design scalable AI systems
Enrolled
Engineering Artificial General Intelligence Systems
FREE
Build cognitive architectures, multi-agent systems, and alignment frameworks for next-generation AI.
Enrolled
AI Governance: Strategy, Policy & Responsible Deployment
FREE
Ensure ethical, compliant, secure AI with governance, risk controls, transparency, fairness and regulatory best practice
Enrolled
Neural Signal Processing & Applied AI
FREE
Learn to analyze neural signals using machine learning and deep learning techniques
EnrolledPrevious Courses
AI in 90 Days: A Leadership Bootcamp
FREE
Create an AI strategy, launch high-value initiatives, and deliver measurable business wins in just 90 days.
Enrolled
AI Security Masterclass: Prompt Injection & LLM Security
FREE
Build, attack, and secure real-world LLM apps with RAG, tool calling, memory, AI agents, and Python.
Enrolled
Claude Fable Masterclass: AI Storytelling & Characters
FREE
Learn Claude Fable to create AI stories, characters, visual worlds, videos, interactive content, and creative projects.
Enrolled
AI Security & Governance Masterclass: Build, Attack & Defend
FREE
Secure AI apps, RAG, tools, memory, and agents while mastering risk, compliance, guardrails, and governance.
Enrolled
7 Days RAG with Python: Build Chatbots That Talk to Data
FREE
Build PDF Chatbots, Semantic Search Engines, Vector Databases, and Enterprise AI Assistants with Python
Enrolled
Build Local AI Apps with Ollama, Python, and Streamlit
FREE
Build private, production-ready AI apps locally with Ollama, Python, Streamlit, RAG, memory, tools, and real projects
Enrolled
AI-Powered Data Analysis: ChatGPT, SQL, Python & BI
FREE
Learn Data Analysis with ChatGPT, Claude, Excel, SQL, Python, Power BI, Prompting & AI Agents
Enrolled
7 Days Certified AI Agents with Python: Autonomous Apps
FREE
Build autonomous AI agents with Python, Ollama, tools, memory, RAG, research, and multi-agent workflows
Enrolled
AI Governance for Business Leaders: Policy to Practice
FREE
Turn AI policies into practical governance, controls, accountability, risk management, and business results.
Enrolled