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/data-drift-monitoring-detection.md

@@ -1,11 +1,14 @@
 # Data Drift Monitoring and Detection
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Data drift er endringer i statistisk fordeling av modellinput-data over tid som kan føre til forringet modellprestasjon. For machine learning-modeller er kontinuerlig overvåking av data drift avgjørende for å opprettholde produksjonskvalitet. Azure Machine Learning tilbyr innebygd drift detection som sammenligner produksjonsdata mot baseline-datasett (typisk treningsdata eller nylig produksjonsdata) og beregner statistiske avstandsmål.
@@ -363,3 +366,31 @@ Hvis kunden bruker legacy `DataDriftDetector` (azureml-datadrift SDK):
 
 **MCP Calls:** 5 (3 × microsoft_docs_search, 1 × microsoft_docs_fetch, 1 × microsoft_code_sample_search)
 **Unique Sources:** 12 Microsoft Learn URLs
+
+
+### Azure ML Model Monitoring — Data Drift Detection (2026)
+
+**Model monitoring signals** (out-of-box for online endpoints):
+
+| Signal | What it detects |
+|--------|----------------|
+| **Data quality** | Null values, out-of-range values, type mismatches in input features |
+| **Data drift** | Statistical distribution change: training data vs production data |
+| **Prediction drift** | Distribution shift in model output predictions |
+| **Feature attribution drift** | Changes in which features drive predictions |
+| **Custom signals** | User-defined metrics via Python scripts |
+
+**Setup options**:
+- **Out-of-box**: Automatically configured for Azure ML online endpoints (no configuration required)
+- **Advanced**: Custom monitoring for models deployed outside Azure ML (batch endpoints, external)
+- **Azure Event Grid integration**: Route monitoring alerts for automated response
+
+**Statistical methods used**:
+- Jensen-Shannon divergence for categorical features
+- Wasserstein distance (Earth Mover's Distance) for numerical features
+- Population Stability Index (PSI) for feature stability
+
+**Reference dataset**: Training dataset used as baseline; monitoring compares production distribution against it.
+
+**Alerting**: Configure thresholds per signal; integrate with Azure Monitor alerts and Action Groups.
+