# Syllabus: AINS6008 AI Project Management & Deployment

## Catalog Description

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

## Course Structure

Each week includes readings, a lecture/slide sequence, an executable lab, and an applied deliverable. Students maintain a reproducible project record and submit work through the LMS or GitHub workflow selected by the instructor.

## Weekly Schedule

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

## Assessment

| Component | Weight |
|-----------|--------|
| Weekly labs and notebooks | 30% |
| Applied assignments | 35% |
| Participation and technical critique | 15% |
| Final synthesis portfolio | 20% |

## Graduate Expectations

Submissions must show technical reasoning, evidence awareness, clear limitations, and responsible use of AI assistance. Code and analysis should be reproducible enough for instructor review.