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/inferencing-optimization-caching.md

@@ -4,6 +4,8 @@
 **Dato:** 2026-02-04
 **Forfattet av:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Inferencing optimization og caching representerer kritiske teknikker for å maksimere ytelse og minimere kostnader når AI-modeller skal serve prediksjoner i produksjon. Mens model training handler om å oppnå høy accuracy, handler inferencing om å levere disse prediksjonene raskt, pålitelig og kostnadseffektivt til brukere og systemer.
@@ -1011,3 +1013,36 @@ Diagnostikk:
 - Monitor **cache hit rate** og **autoscaling metrics** kontinuerlig
 
 **Confidence nivå: HIGH** — Denne referansen er basert på 12 MCP-kall til offisiell Microsoft-dokumentasjon og kodeeksempler.
+
+
+### ONNX Inferencing Optimization for Computer Vision (Azure ML AutoML 2026)
+
+ONNX (Open Neural Network Exchange) enables cross-framework interoperability and inference optimization:
+
+**Supported AutoML computer vision tasks**:
+- Image classification (binary and multi-class)
+- Object detection
+- Instance segmentation
+
+**ONNX inference workflow**:
+1. Download ONNX model files from AutoML training run
+2. Understand model inputs/outputs (image format requirements)
+3. Preprocess data to required input format
+4. Run inference with ONNX Runtime Python API (`onnxruntime`)
+5. Post-process predictions (bounding boxes for detection, masks for segmentation)
+
+**Python ONNX Runtime**:
+```python
+import onnxruntime as rt
+sess = rt.InferenceSession("model.onnx")
+# Works across languages: Python, C++, C#, Java, JavaScript
+```
+
+**Cross-platform benefits**:
+- Deploy on any platform without framework dependencies
+- Reduced inference latency vs Python framework
+- Edge deployment: Azure IoT Edge, on-premises
+- Language flexibility post-export
+
+**SDK**: `azure-ai-ml v2 (current)` — use AutoML image tasks to generate ONNX models automatically
+