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/ci-cd-for-ml-models.md

@@ -1,6 +1,7 @@
 # CI/CD Pipelines for Machine Learning Models
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
@@ -288,6 +289,34 @@ Disse signalene indikerer at din ML CI/CD ikke er production-ready:
 
 ### GitHub Actions Integration
 
+
+### GitHub Actions with Azure Machine Learning (2026 Update)
+The recommended authentication approach is **OpenID Connect (OIDC) with federated credentials** — eliminates long-lived secrets.
+
+**Workflow structure** (`/.github/workflows/`):
+```yaml
+permissions:
+  id-token: write
+jobs:
+  build:
+    steps:
+    - uses: azure/login@v2
+      with:
+        client-id: ${{ secrets.AZURE_CLIENT_ID }}
+        tenant-id: ${{ secrets.AZURE_TENANT_ID }}
+        subscription-id: ${{ secrets.AZURE_SUBSCRIPTION_ID }}
+    - run: az ml job create --file pipeline.yml
+```
+
+**MLOps v2 GitHub setup** (recommended end-to-end):
+1. Fork `Azure/mlops-v2-gha-demo` template repo
+2. Set GitHub secrets: `ARM_CLIENT_ID`, `ARM_CLIENT_SECRET`, `ARM_SUBSCRIPTION_ID`, `ARM_TENANT_ID`
+3. Deploy infrastructure via `tf-gha-deploy-infra.yml` workflow
+4. Run `deploy-model-training-pipeline` and `deploy-online-endpoint-pipeline` workflows
+
+**Pipeline stages**: Prepare Data → Train Model → Evaluate Model → Register Model → Deploy Endpoint
+
+
 **Setup:**
 - Opprett `.github/workflows/` directory i repo
 - Konfigurer GitHub Secrets for Azure credentials (eller OIDC)