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Engineering

This section covers how the SoccerPredictAI system is built, tested, deployed, and operated day-to-day. It spans three tightly coupled concerns:

  • CI/CD — automated quality gates, Docker builds, and Helm-based deployment
  • Dev Workflow & AI Tooling — engineering discipline, Copilot customization layer, audit cycles
  • Runbooks — operational procedures for incident response and recovery

At a Glance

Capability Status
GitLab CI pipeline ✅ Implemented
Docker image build and push ✅ Implemented
Helm-based deployment ✅ Implemented (semi-automated)
Production deployment Manual approval required
Rollback (service) Manual (helm rollback)
Rollback (model) Manual (MLflow alias)
Rollback (data) Manual (dvc checkout)
GitHub Copilot customization layer ✅ Active
Audit cycle ✅ Active

CI/CD

Page Content
CI/CD Overview Pipeline stages, current state, quality gates
Quality Gates Lint, test, security, build checks
Testing Strategy Test pyramid and coverage policy

Dev Workflow & AI Tooling

Page Content
AI Tooling & Customization Copilot instructions, agents, skills, hooks
Audit Cycles Regular review process for AI-assisted changes
Iteration Plans Current sprint and planned work

Runbooks

Page Content
Runbooks Overview Scope and structure
Local Dev Setup Environment setup, first-run procedures
Troubleshooting Common failure scenarios and recovery

Engineering Discipline

Every change — human or AI-assisted — operates within these guardrails:

  • No bypassing Hydra, DVC, or MLflow.
  • No coupling training logic to serving logic.
  • No cross-layer shortcuts (data / features / models / pipelines / app are independent layers).
  • No silent change of public behavior.
  • No claiming planned design as implemented.
  • No new dependencies without justification.

These rules are enforced by code review, CI quality gates, the test suite, and the Copilot customization layer (.github/instructions/, .github/agents/).