Patterns
Implementation control library
Find the implementation control behind the gap.
138 implementation controls mapped to architecture, obligations and evidence. Search the problem; open implementation depth only when you need it.
EAAPL-MDL003 — Canary Model Release
Canary model release is the controlled, incremental exposure of a new AI model to live production traffic, beginning a…
EAAPL-MDL006 — Fine-Tuning Pipeline
Fine-tuning is the process of adapting a pre-trained foundation model to an organisation's specific domain, language,…
EAAPL-MDL008 — Model Access Governance
Model access governance controls who can invoke, export, copy, and administer AI models within and outside the organis…
EAAPL-MDL007 — Model Compression and Optimisation
Model compression reduces the computational footprint of an AI model — memory usage, inference latency, and cost per q…
EAAPL-MDL004 — Model Rollback
Model rollback is the capability to revert production AI model serving to a previously approved, known-good model vers…
EAAPL-MDL001 — Model Versioning and Lineage Tracking
AI models are long-lived, continuously evolving artefacts. Without systematic versioning, organisations cannot answer…
EAAPL-MDL005 — Multi-Model Ensemble
Multi-model ensemble combines the outputs of two or more AI models to produce decisions or responses that are more acc…
EAAPL-MDL002 — Shadow Model Deployment
Shadow model deployment allows an organisation to validate a new AI model under real production conditions — full traf…