# 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

1. **Python project structure for AI** — What makes AI code maintainable beyond a notebook?
2. **Data handling with Python libraries** — How do Python tools support reproducible data work?
3. **Model APIs and reusable components** — How should model code expose clear interfaces?
4. **Testing and continuous integration** — How do we catch AI application failures early?
5. **Application backends and model services** — How does model logic become a service?
6. **User interfaces and workflow integration** — How do users interact with AI outputs in context?
7. **Packaging, environments, and deployment** — How do environments stay reproducible across machines?
8. **End-to-end AI application** — What evidence shows the application works as a system?