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