Architecture

15 AI/ML Architecture Decision Records: Production-Ready, Cloud-Agnostic.

Stop documenting the same AI architecture decisions from scratch. 15 opinionated, production-ready ADRs covering the full AI/ML adoption lifecycle. Every decision includes context, options compared, rationale, and consequences.

Privacy Act 1988 (APP 8)ISO/IEC 42001:2023 (Annex A alignment)DTA AI Ethics Framework
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15 documents: ready to implement.

ADR-ML-001

AI/ML Framework Selection

PyTorch vs TensorFlow vs managed services: cloud-agnostic options comparison

ADR-ML-002

Model Hosting Strategy

Third-party API vs managed endpoint vs self-hosted: tiered by data sensitivity

ADR-ML-003

Training Data Storage and Governance

Object storage + DVC vs managed data platform: Privacy Act aligned

ADR-ML-004

Model Versioning and Registry

MLflow vs cloud-native registry: experiment tracking, promotion workflow

ADR-ML-005

Inference Infrastructure

Serverless vs real-time vs batch: tiered by traffic pattern and latency

ADR-ML-006

AI/ML Observability and Monitoring

Drift detection, fairness monitoring, data quality: cloud-native + open source

ADR-ML-007

Prompt Management and Engineering

Git vs dedicated prompt platform: version control, security, audit trail

ADR-ML-008

RAG vs Fine-tuning vs Base Model

When to use each: cost, data requirements, privacy implications

ADR-ML-009

Human-in-the-Loop Design Pattern

Risk-tiered oversight model: in-the-loop vs on-the-loop vs automated

ADR-ML-010

Output Validation and Guardrails

Layered approach: provider safety + custom business logic + logging

ADR-ML-011

Privacy and APP 8 Compliance

Pseudonymisation vs consent vs sovereign hosting: Privacy Act cross-border obligations

ADR-ML-012

Model Provenance and Supplier Assessment

Tiered supplier assessment: Tier 1/2/3 by production use and data sensitivity

ADR-ML-013

AI Incident Response and Rollback

Canary + automated rollback vs blue/green vs feature flag: per incident severity

ADR-ML-014

Bias, Fairness, and Explainability

SHAP vs LIME vs inherent interpretability: regulatory and legal defensibility

ADR-ML-015

AI Cost Optimisation Strategy

Per-feature tagging, prompt caching, serverless inference: 40-70% cost reduction approaches

What makes this different.

15 complete ADRs in one document

Every ADR includes context, decision drivers, options table with pros and cons, decision, rationale, and consequences.

Privacy Act APP 8 built in

ADR-ML-011 covers Australian cross-border AI processing obligations: pseudonymisation, consent, and sovereign hosting trade-offs.

Cloud-agnostic throughout

Options compare AWS, Azure, GCP, and cloud-agnostic open source alternatives. No single cloud assumed.

ISO 42001 aligned

ADRs reference relevant ISO 42001 Annex A controls where applicable: fairness, security, transparency, and third-party AI.

Editable DOCX

Adapt any ADR to your organisation's context. Modify decisions, add organisation-specific consequences, update dates.

Who this is for Solutions architects, ML engineers, platform teams, and technical leads building AI/ML capabilities.

FAQ

Opinionated. Every ADR includes a specific decision recommendation with rationale. You adapt them to your context: but the work of researching and framing the decision is already done.
Yes. All infrastructure options compare AWS, Azure, GCP, and cloud-agnostic alternatives. No single cloud provider is assumed.
ADRs covering fairness, privacy, human oversight, security, and third-party AI reference the relevant ISO 42001 Annex A controls. They are complementary to but not a substitute for the ISO 42001 documentation packs.

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