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-versioning-registry-management.md

@@ -1,11 +1,14 @@
 # Model Versioning and Registry Management
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Model versioning og registry management er fundamentale komponenter i MLOps-livssyklusen som sikrer sporbarhet, reproduserbarhet og effektiv styring av maskinlæringsmodeller gjennom hele deres levetid. Azure Machine Learning tilbyr to primære tilnærminger: workspace model registry for team-intern bruk og Azure Machine Learning registry for tverrorganisatorisk deling. Begge støtter MLflow som standardformat, noe som gir portabilitet og integrasjon med et bredt økosystem av verktøy.
@@ -31,6 +34,44 @@ Azure Machine Learning skiller seg fra tradisjonelle Git-baserte tilnærminger v
 
 ### Registry-typer sammenlignet
 
+
+### Azure Machine Learning Cross-Workspace Registry (2026)
+
+**Azure ML Registry** enables model, component, and environment sharing across workspaces and subscriptions:
+
+**Two primary scenarios**:
+1. **Cross-workspace MLOps**: Train in `dev` → deploy to `test`/`prod` with full lineage (code, data, environment)
+2. **Cross-team sharing**: Publish models/components to central catalog for reuse across teams
+
+**Registry operations** (CLI v2 / Python SDK v2):
+```bash
+# Create model in registry (from local files)
+az ml model create --name nyc-taxi-model --version 1 --type mlflow_model   --path ./artifacts/model/ --registry-name <registry-name>
+
+# Share model from workspace to registry (preserves training lineage)
+az ml model share --name nyc-taxi-model --version 1   --registry-name <registry-name> --share-with-name <new-name> --share-with-version 1
+
+# Deploy model from registry to any workspace
+# (model: azureml://registries/<registry-name>/models/<name>/versions/<v>)
+az ml online-deployment create --file deploy.yml --all-traffic
+```
+
+**Python SDK pattern**:
+```python
+ml_client_registry = MLClient(credential=credential, registry_name="<REGISTRY_NAME>")
+ml_client_workspace = MLClient(credential=credential, workspace_name="<WS_NAME>", ...)
+
+# Create in registry
+ml_client_registry.models.create_or_update(mlflow_model)
+# Deploy from registry to workspace
+ml_client_workspace.online_deployments.begin_create_or_update(deployment)
+```
+
+**Lineage tracking**: Models registered from job outputs link back to training job, code, data, and environment.
+**Access control**: ACR token-based access (workspace compute has `AcrPull` via registry's managed identity).
+**MLflow format**: Required for no-code deployment with built-in scoring server.
+
+
 | Egenskap | Workspace Registry | Azure ML Registry |
 |----------|-------------------|-------------------|
 | **Scope** | Enkelt workspace | Multi-workspace, cross-subscription |