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1. Technical Standards & Delivery Criteria

Purpose

Definition of what "production-ready" means for AI solutions, with a progressive path from Basic to Advanced to Scalable MLOps.

1. Purpose

This module defines what "production-ready" means for AI solutions, including a realistic pathway:

  • Basic (manual governance, minimal automation)
  • Advanced (more automation, CI/CD/quality gates)

DORA: version control and platforms amplify AI adoption [so-28]

The DORA AI Capabilities Model (2025) shows that strong version control practices and quality internal platforms are among the seven capabilities that amplify the positive impact of AI adoption on performance. Version control acts as the safety net for the higher velocity of change that AI brings; internal platforms provide automated, secure pathways to scale AI benefits. See External Evidence: DORA.

2. Automation Ladder (Realistic Growth Path)

Level Description For whom Example controls
L0 Manual Checklists + manual gates starting teams templates completed, signatures
L1 Semi fixed test set + fixed reporting most teams Objective Card every release
L2 Automated testing tests run automatically on change engineering teams regression test on Golden Set
L3 Governance-as-Code policy checks block release mature MLOps release fails without evidence/metadata

3. Minimum Technical Baseline (Every Team Must Reach)

Reproducibility & version control

  • Code/instructions are in version control (repo)
  • Config (model version, settings) is traceable
  • Release is taggable (RC-1, v1.0) + rollback plan exists

Security & access

  • Secrets not hardcoded; access via secure storage
  • Role-based access (who may change prompts/config?)
  • Least privilege on data sources

Observability (minimum)

  • Logging in place (model version, prompt version, source IDs, output status)
  • Basic metrics: error rate, latency, volume
  • Incident process is known (who calls whom)

Quality & evidence

4. Basic Route (Without Heavy MLOps)

Goal: safely go live with minimal tooling.

  • Use templates as "single source of truth"
  • Plan fixed evaluation moments (e.g. weekly in pilot, monthly in management)
  • Logging minimum: metadata + sampling output (where privacy allows)

5. Advanced Route (With More Automation)

Goal: scalable management with multiple use cases.

  • Automatic regression tests on Golden Set at every change
  • Automatic generation of Validation Report from test runs (where possible)
  • Integration of policy checks: "no Validation Report = no release"

6. Definition of Done for Go-Live

Go-Live Checklist

  • Gate 3 (Production-Ready) approved (Validation Report RC meets Evidence Standards)
  • Logging/retention set up (incl. privacy measures)
  • Incident & rollback procedure tested (tabletop exercise or simulation)
  • Owner for management appointed + monitoring active
  • User instructions + transparency (if relevant) published