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-security/references/performance-scalability/gpu-compute-sizing.md

@@ -1,6 +1,6 @@
 # GPU and Compute Sizing for AI
 
-**Last updated:** 2026-02
+**Last updated:** 2026-04 | Verified: MCP 2026-04
 **Status:** GA
 **Category:** Performance & Scalability
 
@@ -326,6 +326,78 @@ def compare_deployment_options(
     }
 ```
 
+
+## Azure ML Online Endpoints — oppdatert (2026-04)
+
+Azure ML Online Endpoints har to deployment-typer:
+
+| Type | Infrastruktur | Administrasjon | Bruksscenario |
+|------|---------------|----------------|---------------|
+| Managed Online Endpoint | Azure-administrert | Minimal | Raskest å komme i gang, serverless |
+| Kubernetes Online Endpoint | Kundeeid K8s-kluster | Full kontroll | On-premises, hybrid, spesielle krav |
+
+### Anbefalt arbeidsflyt: Lokal debug → Azure deploy
+
+```python
+# Steg 1: Test deployment lokalt
+from azure.ai.ml import MLClient
+from azure.ai.ml.entities import (
+    ManagedOnlineEndpoint,
+    ManagedOnlineDeployment,
+    Model,
+    Environment
+)
+from azure.identity import DefaultAzureCredential
+
+# Lokal testing med Azure ML SDK
+import subprocess
+result = subprocess.run([
+    "az", "ml", "online-endpoint", "create",
+    "--local",
+    "--name", "my-endpoint",
+    "--file", "endpoint.yaml"
+], capture_output=True, text=True)
+
+# Steg 2: Deploy til Azure (ManagedOnlineDeployment)
+ml_client = MLClient(
+    credential=DefaultAzureCredential(),
+    subscription_id="...",
+    resource_group_name="rg-ai",
+    workspace_name="my-ml-workspace"
+)
+
+endpoint = ManagedOnlineEndpoint(
+    name="my-production-endpoint",
+    description="GPU-akselerert inferens",
+    auth_mode="key"
+)
+ml_client.online_endpoints.begin_create_or_update(endpoint).result()
+
+# ManagedOnlineDeployment: spesifiser instance_type for GPU
+deployment = ManagedOnlineDeployment(
+    name="blue",
+    endpoint_name="my-production-endpoint",
+    model="azureml:my-model:1",
+    instance_type="Standard_NC24ads_A100_v4",  # A100 GPU
+    instance_count=2,
+    environment="azureml:my-environment:1",
+    request_settings={
+        "max_concurrent_requests_per_instance": 4,
+        "request_timeout_ms": 90000
+    }
+)
+ml_client.online_deployments.begin_create_or_update(deployment).result()
+```
+
+### GPU-instanstyper for inferens (2026-04)
+
+| SKU | GPU | VRAM | Bruksscenario |
+|-----|-----|------|---------------|
+| `Standard_NC6s_v3` | V100 (1x) | 16 GB | Medium modeller |
+| `Standard_NC24s_v3` | V100 (4x) | 64 GB | Større modeller |
+| `Standard_NC24ads_A100_v4` | A100 (1x) | 80 GB | Store modeller (7B-13B) |
+| `Standard_ND96amsr_A100_v4` | A100 (8x) | 640 GB | Meget store modeller (70B+) |
+
 ## Norsk offentlig sektor
 
 - **Anskaffelse**: GPU VM-er er kostbare — bruk Azure Reserved Instances (1-3 år) for 40-60% besparelse på forutsigbare workloads. Krever godkjenning i anskaffelsesprosess.