Section: tech/ | Subsection: ai-ml/
Alignment: TOGAF ADM | NIST CSF | ISO 27001 | AWS Well-Architected | AI-Native Extensions

Overview

ML pipeline architecture, feature stores, model registries, A/B testing, and LLMOps patterns.

This document is part of the Technology Stack Best Practices body of knowledge within the Ascendion Architecture Best-Practice Library. It provides comprehensive, practitioner-grade guidance aligned to industry standards and extended for AI-augmented, agentic, and LLM-driven design contexts.

Core Principles

1. Intentional Design for AI/ML

Every aspect of ai/ml must be deliberately designed, not discovered after deployment. Document design decisions as ADRs with explicit rationale.

2. Consistency Across the Portfolio

Apply ai/ml practices consistently across all systems. Inconsistent application creates governance blind spots and makes incident investigation unpredictable.

3. Alignment to Business Outcomes

AI/ML practices must demonstrably contribute to business outcomes: reduced downtime, faster delivery, lower operational cost, or improved compliance posture.

4. Evidence-Based Quality Assessment

Quality of ai/ml implementation must be measurable. Define specific metrics and collect evidence continuously — not only at audit or review time.

5. Continuous Evolution

Standards for ai/ml evolve as technology and threat landscapes change. Schedule quarterly reviews of applicable standards and update practices accordingly.

Implementation Guide

Step 1: Current State Assessment

Document the current state of ai/ml practice: what is implemented, what is missing, what is inconsistent across teams. Use the governance/scorecards section for a structured assessment framework.

Step 2: Gap Analysis Against Standards

Compare current state against the standards in this section and applicable frameworks (AWS Well-Architected Framework, Azure Architecture Center). Prioritize gaps by business impact and remediation effort.

Step 3: Design the Target State

Define the target ai/ml state: which patterns will be adopted, which anti-patterns eliminated, which governance mechanisms introduced. Express as a time-bound roadmap.

Step 4: Incremental Implementation

Implement ai/ml improvements incrementally: pilot with one team or system, measure outcomes, refine the approach, then expand. Avoid big-bang transformations.

Step 5: Validate and Iterate

Measure the impact of implemented changes against defined success criteria. Incorporate lessons learned into the practice standards. Contribute improvements back to this library.

Governance Checkpoints

Checkpoint Owner Gate Criteria Status
Current State Documented Solution Architect AI/ML current state assessment completed and reviewed Required
Gap Analysis Reviewed Architecture Review Board Gap analysis reviewed and prioritization approved Required
Implementation Plan Approved Enterprise Architect Target state and roadmap approved by ARB Required
Quality Metrics Defined Solution Architect Measurable success criteria defined for ai/ml improvements Required

Recommended Patterns

Reference Architecture Adoption

Start from an established reference architecture for ai/ml rather than designing from scratch. Adapt to organizational context rather than rebuilding proven foundations.

Pattern Library Contribution

When your team solves a recurring ai/ml problem with a novel approach, document it as a pattern for the library. This compounds organizational knowledge over time.

Fitness Function Testing

Encode ai/ml standards as automated architectural fitness functions — tests that run in CI/CD and fail builds when standards are violated. This makes governance continuous rather than periodic.

Anti-Patterns to Avoid

⚠️ Standards Theater

Documenting ai/ml standards in architecture policies that no one reads and no one enforces. Standards without automated validation or governance gates are not operational standards.

⚠️ Copy-Paste Architecture

Adopting another organization's ai/ml patterns wholesale without adapting to organizational context, team capability, or regulatory environment. Always adapt; never just copy.

AI Augmentation Extensions

AI-Assisted Standards Review

LLM agents analyze design documents against ai/ml standards, generating structured gap reports with cited evidence and suggested remediation approaches.

Note: AI review accelerates governance but does not replace expert architectural judgment. Use as a first-pass filter before human review.

RAG Integration for AI/ML

This section is optimized for vector ingestion into an AI-powered architecture assistant. Semantic search enables architects to retrieve relevant ai/ml guidance through natural language queries.

Note: Reindex the vector store whenever section content is updated to ensure retrieved guidance reflects current standards.

Related Sections

principles/foundational | patterns/structural | governance/review-templates | adrs/platform

References

  1. AWS Well-Architected Framework — aws.amazon.com
  2. Azure Architecture Center — docs.microsoft.com
  3. Google Cloud Architecture Framework — cloud.google.com
  4. Spring Boot Best Practices — IEEE Xplore
  5. Documenting Software Architectures — Bass, Clements, Kazman — Amazon
  6. Building Evolutionary Architectures — Ford, Parsons, Kua — O'Reilly

Last updated: 2025 | Maintained by: Ascendion Solutions Architecture Practice
Section: tech/ai-ml/ | Aligned to TOGAF · NIST · ISO 27001 · AWS Well-Architected

Architecture Diagram
flowchart TD A([🚀 Start: AI/ML]) --> B[Assessment & Discovery] B --> C{Current State\nDocumented?} C -->|No| B C -->|Yes| D[Apply Architecture Principles] D --> D1[Design for Change] D --> D2[Least Privilege] D --> D3[Observability First] D --> D4[AI Augmentation Readiness] D1 & D2 & D3 & D4 --> E[Select Design Patterns] E --> F{NFR Targets\nDefined?} F -->|No| F1[Define NFRs in nfr/] F1 --> F F -->|Yes| G[Document ADRs] G --> H[Architecture Review Board] H --> I{Security\nReview Passed?} I -->|No| I1[Revise Design] I1 --> H I -->|Yes| J{ARB\nApproval?} J -->|Rejected| J1[Address Feedback] J1 --> H J -->|Approved| K[Implementation] K --> L[CI/CD Pipeline] L --> L1[SAST / DAST Scan] L --> L2[Architecture Lint] L --> L3[NFR Validation] L1 & L2 & L3 --> M{All Gates\nPassed?} M -->|No| M1[Fix & Rerun] M1 --> L M -->|Yes| N[Deploy to Production] N --> O[Observability Validation] O --> P[Post-Deployment Review] P --> Q([✅ Governance Record Closed]) style A fill:#4f8ef7,color:#fff style Q fill:#10b981,color:#fff style I1 fill:#fef3c7 style J1 fill:#fef3c7 style M1 fill:#fef3c7