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fix(ms-ai-architect): Foundry URL-navnerom-migrering (ai-foundry → foundry/foundry-classic, 141 filer)

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Task #5 del 1/3 (URL-migrering). Verifiseringen motbeviste STATE.md-premisset om ren prefix-swap: rebrand er per-URL, ikke mekanisk. En blind sed ai-foundry→foundry ville lagd 56 nye 404-er (classic-stiene finnes ikke under nytt foundry/-prefiks — bekreftet empirisk).

Metode: resolverte alle 237 unike KB-URLer mot live redirects (curl -L), bygde full-URL→full-URL-mapping fra faktisk url_effective. Bevarer locale-form, query (?view=) og #fragment per lenke.

- 231 navnerom-erstatninger over 141 filer (408 forekomster):
  - 161 → azure/foundry/ (98 ren prefix-swap + 10 sti-reorg + reorg-tilfeller)
  - 69 → azure/foundry-classic/ (eldre hub-spor: assistants, hub-DR, on-your-data; faktisk redirect-mål per operatorvalg)
  - 1 → azure/foundry-local/

- 2 døde lenker (404) fikset til verifiserte mål:
  - agent-service → azure/foundry/agents/overview
  - concepts/evaluation-evaluators/ → azure/foundry/how-to/evaluate-generative-ai-app

- 5 path-/display-referanser (uten https://, i backticks/lenketekst) rettet manuelt.

- 6 slug-baserte ai-foundry-treff urørt (scope-grense): managed-grafana-dashboard, security-baseline, power-platform prompt-builder, architecture baseline-chat (sistnevnte slug-rebrand i annet navnerom — mulig fremtidig funn).

- Parkert til task #5 del 2/3: Norway East GPT-5-datasuverenitet-fiks + modellkatalog-utvidelse (5.3/5.4/5.5, gpt-oss, sora-2).

Verifisert: 0 gjenværende azure/ai-foundry/-navnerom i skills/. validate-plugin.sh 219 PASS. test-kb-integrity.sh 117/117 passed.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ
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Kjell Tore Guttormsen 2026-06-18 13:37:06 +02:00
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8
skills/ms-ai-engineering/references/mlops-genaiops/model-evaluation-frameworks.md
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@ -473,15 +473,15 @@ Hvis du kjører massive evalueringer (100K+ samples), vurder PTU for judge model
## Kilder og verifisering
### Microsoft Learn (Verified via MCP)
1. [Evaluate generative AI models and applications by using Microsoft Foundry](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/evaluate-generative-ai-app?view=foundry-classic) — **Verified** — Komplett guide til Foundry UI evaluations, metrics, data mapping.
1. [Evaluate generative AI models and applications by using Microsoft Foundry](https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluate-generative-ai-app?view=foundry-classic) — **Verified** — Komplett guide til Foundry UI evaluations, metrics, data mapping.
2. [Evaluation flows and metrics (Azure ML Prompt Flow)](https://learn.microsoft.com/en-us/azure/machine-learning/prompt-flow/how-to-develop-an-evaluation-flow?view=azureml-api-2) — **Verified** — Custom evaluation flows, aggregation nodes.
3. [MLflow 3 Evaluation and Monitoring](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/) — **Verified** — LLM judges, scorers, production monitoring.
4. [Large language model end-to-end evaluation](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/rag/rag-llm-evaluation-phase) — **Verified** — RAG-specific metrics (utilization, completeness, relevance).
5. [Azure AI Evaluation SDK Overview](https://learn.microsoft.com/en-us/python/api/overview/azure/ai-evaluation-readme?view=azure-python) — **Verified** — Python SDK examples, evaluator initialization.
6. [Test and evaluate AI workloads on Azure](https://learn.microsoft.com/en-us/azure/well-architected/ai/test) — **Verified** — Quality metrics, testing vs. evaluation, baselining strategy.
7. [Observability in generative AI](https://learn.microsoft.com/en-us/azure/ai-foundry/concepts/observability) — **Verified** — Three-stage evaluation (base model selection, pre-production, production).
8. [Azure OpenAI Evaluation API](https://learn.microsoft.com/en-us/azure/ai-foundry/openai/how-to/evaluations?view=foundry-classic) — **Verified** — REST API, testing criteria, grading process.
9. [GitHub Action for Evaluation](https://learn.microsoft.com/en-us/azure/ai-foundry/how-to/evaluation-github-action?view=foundry-classic) — **Verified** — CI/CD integration.
7. [Observability in generative AI](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability) — **Verified** — Three-stage evaluation (base model selection, pre-production, production).
8. [Azure OpenAI Evaluation API](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/evaluations?view=foundry-classic) — **Verified** — REST API, testing criteria, grading process.
9. [GitHub Action for Evaluation](https://learn.microsoft.com/en-us/azure/foundry/how-to/evaluation-github-action?view=foundry-classic) — **Verified** — CI/CD integration.
10. [Scorers and LLM judges (MLflow 3)](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/eval-monitor/concepts/scorers) — **Verified** — Judge models, accuracy validation, partner-powered AI disclaimers.
### Confidence per seksjon