# Module 4 Overview

## Theme

Testing and continuous integration

## Essential Question

How do we catch AI application failures early?

## Module Components

- `Book prose`: conceptual framing, domain scenario, methods, and failure modes
- `Assignment`: evidence-backed production of a specific artifact
- `Slides`: presentation sequence for seminar or lecture delivery
- `Narration`: spoken version of the slide flow
- `Instructor notes`: facilitation plan, discussion prompts, and grading cues
- `Rubric`: criteria for evaluating the module artifact
- `Notebook`: executable lab aligned with the module theme using synthetic API requests, validation outcomes, latency measurements, and test-case results

## Module Artifact

tested Python AI component with interface contract, CI evidence, and deployment notes focused on testing and continuous integration: Add unit and integration tests for a model pipeline.

## Professional Setting

Students work as if advising an engineering team converting prototype AI code into a maintainable application component. Their work must be intelligible to software engineer, ML engineer, QA lead, product owner, and operations reviewer.