Module 5 Overview

Theme

MLOps and release management

Essential Question

How do models move safely from experiment to production?

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 mlops and release management: Design a release pipeline with rollback.

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.