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-fundamentals-overview.md

@@ -1,11 +1,14 @@
 # MLOps Fundamentals - Lifecycle and Principles
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Machine Learning Operations (MLOps) er anvendelse av DevOps-prinsipper på machine learning-prosjekter. Målet er å automatisere og effektivisere hele ML-livssyklusen – fra eksperimentering og trening, via deployment, til overvåking og retrening. MLOps bygger på etablert DevOps-praksis som continuous integration (CI), continuous deployment (CD), version control, og infrastructure as code (IaC), men legger til ML-spesifikke utfordringer som data versioning, model tracking, feature engineering, og drift detection.
@@ -292,6 +295,49 @@ jobs:
 
 ### DevOps-verktøy
 
+
+### DevOps for Machine Learning — Azure DevOps Integration (2026)
+
+**Azure Pipelines + Azure ML** (how-to-devops-machine-learning):
+
+Automate the ML lifecycle via Azure DevOps pipelines:
+1. Data preparation (ETL)
+2. On-demand scale-out training
+3. Model deployment (public/private web service)
+4. Monitoring (performance, data drift)
+
+**Azure DevOps pipeline YAML pattern**:
+```yaml
+- task: AzureCLI@2
+  inputs:
+    azureSubscription: $(service-connection)
+    inlineScript: |
+      job_name=$(az ml job create --file pipeline.yml -g $(resource-group) -w $(workspace) --query name -o tsv)
+      echo "##vso[task.setvariable variable=JOB_NAME;isOutput=true;]$job_name"
+
+- job: WaitForJobCompletion
+  pool: server  # Server job — no agent costs
+  steps:
+  - task: AzureMLJobWaitTask@1  # From Azure ML extension
+    inputs:
+      serviceConnection: $(service-connection)
+      azureMLJobName: $(azureml_job_name)
+```
+
+**Authentication options**:
+- Azure Resource Manager service connection (recommended with Azure ML extension)
+- Generic service connection (uses InvokeRESTAPI task)
+
+**MLOps maturity model**: Manual → Partial automation → Full CI/CD → Full MLOps with monitoring
+
+**Key automation operations** (Azure DevOps):
+- Infrastructure deployment (Terraform / Bicep)
+- Component registration and versioning
+- Model training on compute clusters
+- Online/batch endpoint deployment
+- Production monitoring alerts
+
+
 | Verktøy | Kostnad | Anbefaling |
 |---------|---------|-----------|
 | **Azure DevOps** | Gratis for 5 brukere + 1800 min/mnd pipeline | Bruk Basic plan for mindre team |