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/model-evaluation-frameworks.md

@@ -1,11 +1,14 @@
 # Model Evaluation Frameworks and Metrics
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Evaluering av AI-modeller, spesielt generative AI-applikasjoner, krever en helt annen tilnærming enn tradisjonell maskinlæring. Mens tradisjonell ML fokuserer på deterministiske metrikker som accuracy og precision, må GenAI-evaluering håndtere multi-turn-samtaler, kontekstuell relevans, sikkerhet og subjektiv kvalitet. Microsoft tilbyr et omfattende rammeverk for modellevaluering gjennom Azure AI Foundry, Azure Machine Learning Prompt Flow og MLflow 3, som dekker hele utviklingsløpet fra modellvalg til produksjonsovervåking.
@@ -107,6 +110,42 @@ print(f"Foundry URL: {result.get('studio_url')}")
 
 ### MLflow 3 Evaluation & Monitoring
 
+
+### MLflow 3 Evaluation Framework (2026)
+
+MLflow 3 provides the evaluation framework for both traditional ML and GenAI applications on Databricks:
+
+**Scorer types** (unified interface for all evaluation):
+
+| Type | Customization | Use Case |
+|------|--------------|---------|
+| Built-in judges | Minimal | Quick evaluation: `Correctness`, `RetrievalGroundedness`, `Safety` |
+| Guidelines judges | Moderate | Custom natural-language rules (pass/fail) |
+| Custom LLM judges | Full | Domain-specific criteria, detailed scoring |
+| Code-based scorers | Full | Deterministic: exact match, format validation, business logic |
+
+**Key evaluation functions**:
+```python
+import mlflow
+
+# Development evaluation
+results = mlflow.genai.evaluate(
+    data=eval_dataset,
+    scorers=[RelevanceToQuery(), RetrievalGroundedness(), Correctness()]
+)
+
+# Production monitoring — same scorers as development
+# Automatically applied to production traces
+```
+
+**Judge accuracy**: Databricks validates with Cohen's Kappa, accuracy, F1 score against human expert judgment.
+
+**Traditional ML evaluation** (Azure ML):
+- Data quality signals: null rate, out-of-bounds, type errors
+- Statistical drift: Jensen-Shannon divergence, Wasserstein distance
+- Custom metrics via Python scripts in monitoring jobs
+
+
 MLflow 3 integrerer evaluering og production monitoring i én workflow. Samme LLM judges og scorers kan brukes i development, testing og production.
 
 **Hovedkomponenter:**