AINS6007: Applied AI Programming with Python#
Aurnova MSAI track: Core
Credits: 3
Format: 8-week online graduate course
Develops maintainable Python AI applications with testing, APIs, interfaces, packaging, and deployment.
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 Applied AI Programming with Python;
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#
Python project structure for AI — What makes AI code maintainable beyond a notebook?
Data handling with Python libraries — How do Python tools support reproducible data work?
Model APIs and reusable components — How should model code expose clear interfaces?
Testing and continuous integration — How do we catch AI application failures early?
Application backends and model services — How does model logic become a service?
User interfaces and workflow integration — How do users interact with AI outputs in context?
Packaging, environments, and deployment — How do environments stay reproducible across machines?
End-to-end AI application — What evidence shows the application works as a system?