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.