Module 5: MLOps and release management

Module 5: MLOps and release management#

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

  • 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.

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-5.

  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.