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/feedback-loops-continuous-improvement.md

@@ -4,6 +4,8 @@
 **Dato:** 2026-02-04
 **Confidence:** HIGH (basert på offisiell Microsoft-dokumentasjon)
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Feedback loops og kontinuerlig forbedring er kritiske komponenter i moderne AI-operasjoner. I motsetning til tradisjonell programvare, hvor funksjonalitet er deterministisk, kan AI-modeller vise kvalitetsdrift eller uventet oppførsel når de møter reelle data. Et velfungerende feedback-system sikrer at modeller forblir nøyaktige, relevante og trygge gjennom hele sin livssyklus.
@@ -738,3 +740,32 @@ Dette dokumentet dekker hele feedback loop-syklusen for både classical ML og Ge
 5. **Kostnad:** Threshold-based retraining kan spare 50-70% compute vs daily retraining
 
 Bruk arkitekturmønstrene til å visualisere løsningen for kunden. Påpek at MLflow Tracing + Agent Evaluation gir "free" observability (built-in i Databricks).
+
+
+### MLflow 3 Evaluation & Feedback Loop (2026)
+
+MLflow 3 introduces a unified evaluation-monitoring lifecycle for GenAI feedback loops:
+
+**Iterative workflow**:
+1. **Trace** production requests (MLflow Tracing — end-to-end observability)
+2. **Evaluate** against scorers during development (`mlflow.genai.evaluate()`)
+3. **Monitor** production with same scorers (consistent quality measurement)
+4. **Gather human feedback** via Review App (expert annotations)
+5. **Improve** prompts/models based on evaluation datasets
+
+**Azure ML Model Monitoring signals**:
+- Data quality: null values, out-of-range, type mismatch
+- Data drift: statistical distribution changes between training and production data
+- Prediction drift: distribution shift in model outputs
+- Feature attribution drift: changes in feature importance
+- Custom signals: user-defined metrics via custom scripts
+
+**Monitoring setup**:
+```python
+# Set up out-of-box monitoring for Azure ML online endpoints
+# Monitors data drift, prediction drift automatically
+# Integrates with Azure Event Grid for alerting
+```
+
+**Continuous improvement cycle**: Production traces → MLflow evaluation datasets → Scorer alignment → Prompt/model update → A/B test → Production rollout
+