docs(architect): weekly KB update — 106 files refreshed (2026-04)

Updates across all 5 skills: ms-ai-advisor, ms-ai-engineering,
ms-ai-governance, ms-ai-security, ms-ai-infrastructure.

Key changes:
- Language Services (Custom Text Classification, Text Analytics, QnA):
  retirement warning 2029-03-31, migration guides to Foundry/GPT-4o
- Agentic Retrieval: 50M free reasoning tokens/month (Public Preview)
- Computer Use: Claude Sonnet 4.5 (preview) + OpenAI CUA models
- Agent Registry: Risks column (M365 E7), user-shared/org-published types
- Declarative agents: schema v1.5 → v1.6, Store validation requirements
- MLflow 3: 13 built-in LLM judges, production monitoring, Genie Code
- AG-UI HITL: ApprovalRequiredAIFunction (C#) + @tool(approval_mode) (Python)
- Entra ID Ignite 2025: Agent ID Admin/Developer RBAC roles, Conditional Access
- Security Copilot: 400 SCU/month per 1000 M365 E5 licenses, auto-provisioned
- Fast Transcription API: phrase lists, 14-language multi-lingual transcription
- Azure Monitor Workbooks: Bicep support, RBAC specifics
- Power Platform Copilot: data residency (Norway/Europe → EU DB, Bing → USA)
- RAG security-rbac: 4-approach table (GA + 3 preview access control methods)
- IaC MLOps: Well-Architected OE:05 principles, Bicep/Terraform patterns
- Translator: image file batch translation Preview (JPEG/PNG/BMP/WebP)

All 106 files: Last updated 2026-04 | Verified: MCP 2026-04

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

skills/ms-ai-engineering/references/mlops-genaiops/infrastructure-as-code-mlops.md

@@ -4,6 +4,8 @@
 **Kategori:** MLOps & GenAIOps
 **Forfatter:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Infrastructure as Code (IaC) er en fundamental MLOps-praksis der infrastruktur defineres og deployes gjennom kode fremfor manuelle konfigurasjoner. Dette er kritisk viktig for AI/ML-prosjekter fordi det sikrer reproducerbarhet, konsistens og versjonskontroll av hele ML-miljøet — fra development til production.
@@ -539,6 +541,42 @@ resource mlWorkspace 'Microsoft.MachineLearningServices/workspaces@2024-01-01-pr
 
 ### IaC-verktøy kostnader
 
+
+### IaC Design for MLOps — Azure Well-Architected (OE:05) 2026
+
+**Core principle** (Well-Architected OE:05): Standardized IaC approach with declarative syntax, consistent styles, appropriate modularization, quality assurance.
+
+**Declarative over imperative** (recommended):
+- Bicep / ARM templates: Azure-native, JSON/DSL declarative
+- Terraform: Industry-standard, multi-cloud declarative
+- Avoid: imperative scripts for infrastructure state management
+
+**Azure-native tools**:
+```bash
+# Bicep — deploy Azure ML workspace
+az deployment group create --template-file ml-workspace.bicep
+
+# Terraform — integrated into GitHub Actions / Azure Pipelines
+terraform init && terraform apply
+```
+
+**Layered IaC pipeline approach for MLOps**:
+- **Low-touch** (networking, VNet, ACR): Rarely changes, stable baseline
+- **Medium-touch** (compute clusters, storage, AKS): Occasional changes
+- **High-touch** (model endpoints, deployments): Continuous delivery
+
+**IaC best practices**:
+- Treat IaC artifacts the same as application code (version control, PR reviews, testing)
+- Use parameters/variables for multi-environment support (dev/test/prod)
+- Collocate IaC with application code for synchronized deployments
+- Scan IaC repos for secrets (Microsoft Defender for Cloud: IaC vulnerability scanning)
+- Immutable infrastructure preferred for business-critical workloads
+
+**AI opportunity** (2026): AI tools (GitHub Copilot) can review IaC templates, identify misconfigurations, suggest security improvements, and generate templates from natural language.
+
+**MLOps v2 infrastructure**: `tf-gha-deploy-infra.yml` workflow in `Azure/mlops-v2-gha-demo` deploys full Azure ML infrastructure via Terraform + GitHub Actions.
+
+
 | Verktøy | Lisens | Kostnad |
 |---------|--------|---------|
 | **Bicep** | Open source (MIT) | Gratis |