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Artificial Intelligence

AI Release Guardrails Module

An advanced lecturer-ready module for teaching AI-flavoured CI/CD through daily automation, validation evidence, release gates, and post-release observation.
Level
Advanced
Format
Module
Published
April 28, 2026
Artificial Intelligence: AI Release Guardrails ModuleAn AI lifecycle for AI Release Guardrails Module, showing data, model evaluation, and human release review.DATAci/cdEVALUATEmlopsREVIEWevaluation
Artificial IntelligenceEvaluation lifecycle
Learn

Build the core model

Read the lesson and connect each concept to the visual model above.

This module helps students understand that automation should not be confused with trustworthiness. Even a daily website content release still needs evidence, reproducibility, and rollback readiness before it reaches users.

Included teaching examples

- name: Publish queued content
  run: npm run content:daily

- name: Validate release candidate
  run: npm run validate:content && npm run build

- name: Deploy after successful CI
  if: github.event.workflow_run.conclusion == 'success'

These examples support discussion about when an automated system should be blocked from release even if the scheduler itself is working correctly.

Diagram support

The paired SVG diagram visualises evaluation gates, approval logic, deployment, and post-release monitoring through the lens of a practical website automation pipeline.

Suggested learning flow

  1. Compare code-only automation with evidence-based release control.
  2. Define what counts as a trustworthy release gate.
  3. Discuss rollback conditions for weak or unsafe output.
  4. Turn post-release observation into a responsible AI discussion.