Module 4 Overview#

Theme#

Evaluation plans and acceptance criteria

Essential Question#

What evidence authorizes forward movement?

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 project telemetry with scope volatility, evaluation results, risks, adoption readiness, and operational load

Module Artifact#

deployment decision package with project charter, acceptance gates, risk log, and monitoring plan focused on evaluation plans and acceptance criteria: Define acceptance tests for model and workflow quality.

Professional Setting#

Students work as if advising a delivery team deciding whether an AI project should move from experiment to deployment. Their work must be intelligible to project sponsor, product owner, ML lead, operations manager, and governance reviewer.