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/azure-ml-pipelines-orchestration.md

@@ -1,11 +1,14 @@
 # Azure ML Pipelines - Orchestration and Automation
 
 **Last updated:** 2026-02
+**Verified:** MCP 2026-04
 **Status:** GA
 **Category:** MLOps & GenAIOps
 
 ---
 
+**Verified:** MCP 2026-04
+
 ## Introduksjon
 
 Azure Machine Learning pipelines representerer et komplett orkestreringsrammeverk for machine learning-arbeidsflyter. En pipeline automatiserer en komplett ML-oppgave ved å dele den inn i flere håndterbare steg (components), hvor hvert steg kan utvikles, optimaliseres, konfigureres og automatiseres uavhengig. Azure ML håndterer dependencies mellom steg automatisk, og legger til rette for parallellisering, caching og gjenbruk.
@@ -18,6 +21,61 @@ Fra et kostnads- og effektivitetsperspektiv gir pipelines betydelige fordeler: d
 
 ### Pipeline Components (v2)
 
+
+### Azure ML Pipelines — Python SDK v2 (Tutorial 2026)
+
+**Key benefits**: Standardized MLOps, scalable team collaboration, training efficiency, cost reduction.
+
+**Pipeline creation pattern** (SDK v2):
+```python
+from azure.ai.ml import MLClient, dsl, Input, Output, command
+from azure.identity import DefaultAzureCredential
+
+ml_client = MLClient(DefaultAzureCredential(), subscription_id, resource_group, workspace)
+
+# 1. Create reusable components
+data_prep_component = command(
+    name="data_prep",
+    inputs={"data": Input(type="uri_folder"), "test_train_ratio": Input(type="number")},
+    outputs={"train_data": Output(type="uri_folder"), "test_data": Output(type="uri_folder")},
+    code="./components/data_prep",
+    command="python data_prep.py --data ${{inputs.data}} ...",
+    environment=f"{env.name}:{env.version}",
+)
+# Register for reuse
+ml_client.create_or_update(data_prep_component.component)
+
+# 2. Define pipeline with @dsl.pipeline decorator
+@dsl.pipeline(compute="serverless", description="E2E training pipeline")
+def training_pipeline(data_input, test_train_ratio, learning_rate, model_name):
+    prep_job = data_prep_component(data=data_input, test_train_ratio=test_train_ratio)
+    train_job = train_component(
+        train_data=prep_job.outputs.train_data,
+        test_data=prep_job.outputs.test_data,
+        learning_rate=learning_rate,
+        registered_model_name=model_name,
+    )
+
+# 3. Submit pipeline
+pipeline_job = ml_client.jobs.create_or_update(
+    training_pipeline(data_input=..., ...),
+    experiment_name="e2e_pipeline"
+)
+```
+
+**Component lifecycle**:
+1. Write YAML spec or create programmatically (`CommandComponent`)
+2. Register with name+version in workspace or registry
+3. Load and compose into pipeline
+4. Submit via `ml_client.jobs.create_or_update()`
+
+**Compute options**: `serverless` (recommended), named compute cluster, or per-step compute override.
+**Environment**: Curated environments (`azureml://registries/azureml/environments/sklearn-1.5/labels/latest`) or custom conda/Docker.
+**Output types**: `uri_folder` (data), `mlflow_model` (model), `uri_file` (file).
+
+**MLflow integration**: Use `mlflow.start_run()` in scripts for automatic experiment tracking (metrics, parameters, models).
+
+
 | Komponent-type | Beskrivelse | Bruksområde |
 |----------------|-------------|-------------|
 | **Command component** | Kjører et shell-script eller Python-script | Data prep, training, scoring, evaluation |