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/mlops-teams-collaboration-tools.md

@@ -5,6 +5,8 @@
 **Kilde:** Microsoft Learn, Azure Architecture Center
 **Konfidensgradering:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Vellykkede MLOps-implementeringer krever samarbeid mellom flere teamroller med ulike verktøy, arbeidsflyter og ansvar. Denne referansen dekker hvordan ulike personas samarbeider gjennom machine learning-livssyklusen, hvilke verktøy som støtter samarbeid, og hvordan organisasjoner kan strukturere teamarbeid for maksimal effektivitet.
@@ -123,6 +125,32 @@ MLOps-miljøer opererer med distinkte roller som hver har spesifikke ansvarsomr
 **Konfidensmarkør:** ⭐⭐⭐⭐⭐ Azure Boards er core DevOps-plattform med native Azure DevOps-integrasjon.
 
 #### Azure DevOps / GitHub Actions
+
+
+### Azure DevOps — Integrated MLOps Platform (2026)
+
+Azure DevOps provides end-to-end project management for ML teams:
+
+| Service | ML Use Case |
+|---------|-------------|
+| **Azure Boards** | Sprint planning for model iterations, bug tracking, backlog management |
+| **Azure Repos** | Git repositories for model code, notebooks, IaC; branch policies + PR reviews |
+| **Azure Pipelines** | CI/CD for ML (build, test, train, deploy); integrates with Azure ML via `AzureMLJobWaitTask@1` |
+| **Azure Test Plans** | Manual testing of model outputs, test case management |
+| **Azure Artifacts** | Package feeds (NuGet, pip, conda) for ML libraries and shared components |
+
+**Azure DevOps MCP Server**: Natural language queries for project management — `Summarize sprint status`, `List blocked work items`, `Show pipeline success rates` (2026 feature).
+
+**GitHub Actions integration** (alternative to Azure Pipelines):
+- OIDC authentication (recommended, no long-lived secrets)
+- `azure/login@v2` + `az ml job create` pattern
+- MLOps v2 solution accelerator: `Azure/mlops-v2-gha-demo`
+
+**Databricks CI/CD best practices**:
+- Feature branching with short-lived branches
+- Automated notebook testing before merge
+- MLflow experiment tracking integrated into PR workflows
+
 **Formål:** CI/CD automation for ML lifecycle
 **Nøkkelkapabiliteter:**
 - Pipeline-basert workflow automation