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/model-deployment-strategies-azure.md

@@ -5,6 +5,8 @@
 **Målgruppe:** Arkitekter som planlegger ML-modellutplassering i produksjon
 **Konfidensgrad:** ⚡️⚡️⚡️ Høy (basert på Microsoft Learn + offisielle code samples)
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Model deployment strategies handler om hvordan man trygt og effektivt ruller ut nye ML-modeller eller modellversjoner til produksjon uten å forårsake nedetid eller forringet brukeropplevelse. Azure Machine Learning tilbyr flere deployment patterns som støtter **progressive exposure**, **traffic routing**, og **rollback-mekanismer**.
@@ -1050,3 +1052,36 @@ Denne kunnskapsreferansen er basert på følgende Microsoft Learn-artikler og co
 
 **Sist oppdatert:** 2026-02-04
 **Neste review:** 2026-05-04 (eller ved større endringer i Azure ML deployment capabilities)
+
+
+### Safe Rollout / Blue-Green Deployment (Azure Well-Architected 2026)
+
+Azure ML managed online endpoints support blue-green (safe rollout) deployments natively:
+
+```bash
+# Deploy green deployment with 0% traffic initially
+az ml online-deployment create --name green --endpoint my-endpoint --traffic-allocation 0
+
+# Test green deployment in isolation (direct routing)
+az ml online-endpoint invoke --name my-endpoint --deployment-name green
+
+# Mirror 10% of live traffic to green for shadow testing
+# Then progressively shift: 10% → 50% → 100%
+az ml online-endpoint update --name my-endpoint --traffic blue=90 green=10
+```
+
+**Azure Well-Architected SDP principles (OE:11)**:
+- **Progressive exposure**: Canary → Blue-Green → Deployment Stamps
+- **Health models**: Pass health checks before each rollout phase
+- **Bake time**: Hours/days between phases (not minutes) to capture time-zone usage patterns
+- **Failure detection**: Automatic halt + investigation when health signals degrade
+- **Recovery options**: Roll back (revert), roll forward (hotfix), or redeploy last known good
+
+**Azure facilitation**:
+- `Azure Pipelines` + `GitHub Actions` support multi-stage deployments with approval gates
+- `Azure App Configuration` for feature flag management
+- `Azure Load Balancers` for traffic routing and health monitoring
+- Point-in-time restore available for Azure SQL, Cosmos DB, MySQL, PostgreSQL
+
+**Emergency SDP**: Prescriptive protocols for hotfix acceleration — approval stage and bake time reduction — with explicit approval criteria.
+