# AINS6008: AI Project Management & Deployment

**Aurnova MSAI track:** Core  
**Credits:** 3  
**Format:** 8-week online graduate course

Covers AI project scoping, stakeholder management, empirical delivery, MLOps, adoption, and operations.

This course follows the Aurnova/Castalia course-site pattern used by AINS6003: each module includes book prose, an assignment notebook, slide notebook, narration, instructor notes, and an executable lab.

## Course Outcomes

By the end of the course, students will be able to:

- explain the major concepts and tradeoffs in AI Project Management & Deployment;
- build or evaluate applied AI artifacts aligned with the course domain;
- document assumptions, evidence, limitations, and operational risks;
- connect technical work to governance, stakeholder needs, and deployment readiness.

## Module Map

1. **AI product discovery and scoping** — Which problem is worth solving with AI?
2. **Stakeholders, requirements, and risk** — Who is affected, and what must the system not do?
3. **Agile delivery for AI uncertainty** — How do AI projects adapt when results are empirical?
4. **Evaluation plans and acceptance criteria** — What evidence authorizes forward movement?
5. **MLOps and release management** — How do models move safely from experiment to production?
6. **Change management and adoption** — Why do technically valid AI projects fail in organizations?
7. **Operations, monitoring, and governance** — What keeps deployed AI accountable over time?
8. **Deployment business case** — How should leaders decide whether to launch?