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Lifecycle & Versioning

Every AI Product exists within a lifecycle — from conception to retirement.
Lifecycle and versioning declarations provide predictability, accountability, and continuity across time.

Why Lifecycle & Versioning Matter​

  • Traceability → Consumers must know which version they are using.
  • Governance → Regulations require full lifecycle documentation.
  • Reliability → Clear stages prevent premature adoption of unstable products.
  • Continuity → Versioning ensures backward compatibility or declared breaking changes.
  • Trust → Lifecycle transparency prevents “black box” deployments.

Lifecycle Stages​

AI Products must declare their current stage:

  1. Experimental → Prototype or research-only.
  2. Beta → Early consumer adoption; limited SLAs.
  3. Production → Fully supported with declared SLAs.
  4. Deprecated → Still available, but scheduled for removal.
  5. Retired → No longer available or maintained.

Versioning Requirements​

  • Semantic Versioning (SemVer) recommended: MAJOR.MINOR.PATCH.

    • MAJOR → Breaking changes (e.g., input schema change).
    • MINOR → New features, backward-compatible.
    • PATCH → Bug fixes or minor improvements.

  • Version Metadata must declare:

    • Release date.
    • Lifecycle stage.
    • Compatibility notes.
    • Deprecation warnings (if applicable).

  • Provenance Linkage → Versions must link to Lineage & Provenance.

Retirement Obligations​

When an AI Product is retired:

  • Consumers must be notified through catalog and API signals.
  • Metadata must persist for audit and lineage purposes.
  • Successor products (if any) must be referenced explicitly.

Example​

AI Product: Multilingual Summarizer

  • Version: 2.1.0 (Production).
  • Release Date: 2025-06-01.
  • Compatibility: Backward-compatible with v2.0 inputs/outputs.
  • Deprecation Notice: v1.x series deprecated; retirement scheduled for 2026-01.
  • Lineage: Fine-tuned LLaMA model on multilingual corpus v3.

Summary​

  • Lifecycle stages ensure clarity on product maturity.
  • Semantic versioning ensures traceability and stability.
  • Retirement obligations preserve lineage and accountability.

Principle: An AI Product without lifecycle and versioning declarations is unstable — it cannot be reliably adopted or governed.