Module 8: End-to-end AI application

Module 8: End-to-end AI application#

Theme#

End-to-end AI application

Essential Question#

What evidence shows the application works as a system?

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

  • 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 end-to-end ai application: Ship a documented AI application with tests and demo data.

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.

Use This Module in Order#

  1. Read the learning chapter.

  2. Review the slide deck with the matching narration.

  3. In Populi, open the private student-repository link for this course and enter modules/module-8.

  4. Clone the repository once or open its Codespace/Colab copy; run lab.ipynb and complete exercise.ipynb there.

  5. Self-check with the rubric, commit and push the work, then submit exactly what Populi requests.