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/monitoring-observability-ml-systems.md

@@ -7,6 +7,8 @@
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Monitoring og observability for ML-systemer handler om kontinuerlig overvåkning av modeller i produksjon for å sikre ytelse, kvalitet og pålitelighet. Azure tilbyr et komplett økosystem for ML-overvåkning gjennom Azure Machine Learning Model Monitoring og Azure Monitor, som til sammen gir innsikt i både **modellytelse** (data science-perspektiv) og **operasjonell helse** (infrastruktur-perspektiv).
@@ -297,6 +299,45 @@ create_monitor:
 
 ### Azure Monitor
 
+
+### Azure Machine Learning Monitoring Architecture (2026)
+
+**Azure Monitor integration**:
+- All metrics in namespace: `Machine Learning Service Workspace`
+- Platform metrics collected automatically, no configuration needed
+- Route resource logs to Log Analytics for querying with KQL
+
+**Key Kusto (KQL) queries**:
+```kusto
+# Failed jobs last 5 days
+AmlComputeJobEvent
+| where TimeGenerated > ago(5d) and EventType == "JobFailed"
+| project TimeGenerated, ClusterId, EventType, ExecutionState, ToolType
+
+# Failed online endpoint requests
+AmlOnlineEndpointTrafficLog
+| where TimeGenerated > ago(1d) and ResponseCode != 200
+| project TimeGenerated, EndpointName, DeploymentName, ResponseCode
+```
+
+**Recommended alert rules**:
+| Alert | Condition | Threshold |
+|-------|-----------|-----------|
+| Model Deploy Failed | Total > 0 | Any failure |
+| Quota Utilization | Average > 90% | High utilization |
+| Unusable Nodes | Total > 0 | Any unusable |
+
+**Application Insights integration**: Live metrics, Transaction search, Failures, Performance analysis.
+Use workspace-based Application Insights (default for new workspaces) + Azure Monitor Private Link for VNet isolation.
+
+**Data storage layers**:
+- Metrics database: Platform metrics (near real-time)
+- Log Analytics: Resource logs + Activity log (queryable with KQL)
+- Azure Storage / Event Hubs: Long-term export
+
+**Cross-workspace monitoring**: Use single Log Analytics workspace for multiple Azure ML workspaces to query across all resources simultaneously.
+
+
 **Application Insights** (📊 For endpoints):
 ```python
 # Enable for online endpoint