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feat(ms-ai-architect): decision-b Enhet 1 — dialekt-relabel 51 filer (Kategori→Category + Sist oppdatert→Last updated), 73 byte-eksakte swaps [skip-docs]

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Ren value-preserving label-relabel av de to norske header-labelene til engelsk på 51 ref-filer (22 bærer begge). Ny testet ren primitiv relabelHeaderDialect() (header-blokk-scoped, kollisjons-/multiforekomst-guard) + manifest-drevet driver relabel-dialect.mjs (frosset 51-fil-manifest, hard per-fil-invariant, idempotent, isMain-guard). **Dato:** bevisst UTE (body-template-felle → Enhet 2). Premiss-korreksjon i roadmap R22: tredje Dato-dialekt (16), 0 bold-duplikater (ikke 4), 1 datoløs (ikke 5), category-none = vindus-artefakt. test-relabel-dialect 11/11; diff +73/-73 0 linjer utover label; suite 782/782 exit 0.
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Kjell Tore Guttormsen 2026-07-06 10:07:52 +02:00
commit e999b74eda
54 changed files with 405 additions and 75 deletions
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skills/ms-ai-engineering/references/mlops-genaiops/cost-optimization-mlops-pipelines.md
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# Kostnadsoptimalisering i MLOps-pipelines
**Dato:** 2026-04
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Relevans:** Azure Machine Learning, MLOps-implementering, FinOps for AI
**Type:** reference
**Source:** https://learn.microsoft.com/azure/machine-learning/concept-ml-pipelines

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skills/ms-ai-engineering/references/mlops-genaiops/feedback-loops-continuous-improvement.md
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# Feedback Loops and Continuous Improvement
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Dato:** 2026-02-04
**Last updated:** 2026-06-24
**Confidence:** HIGH (basert på offisiell Microsoft-dokumentasjon)

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skills/ms-ai-engineering/references/mlops-genaiops/genaiops-llm-specific-practices.md
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**Dato:** 2026-02-04
**Last updated:** 2026-06-19
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Konfidensgrad:** Høy (basert på 18 MCP-kilder fra Microsoft Learn)
**Type:** reference
**Source:** https://learn.microsoft.com/python/api/overview/azure/ai-evaluation-readme

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skills/ms-ai-engineering/references/mlops-genaiops/governance-audit-ml-operations.md
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# Governance and Audit Trails in MLOps
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Dato:** 2026-06-19
**Confidence:** 95% (High — bygger på offisiell Microsoft-dokumentasjon og Azure-referansearkitekturer)
**Type:** reference

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skills/ms-ai-engineering/references/mlops-genaiops/inferencing-optimization-caching.md
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# Inferencing Optimization and Caching
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Dato:** 2026-06-19
**Forfattet av:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
**Type:** reference

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skills/ms-ai-engineering/references/mlops-genaiops/infrastructure-as-code-mlops.md
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# Infrastructure as Code for MLOps
**Dato:** 2026-06-19
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Forfatter:** Cosmo Skyberg, Senior Microsoft AI Solution Architect
**Type:** reference
**Source:** https://learn.microsoft.com/azure/aks/concepts-machine-learning-ops

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skills/ms-ai-engineering/references/mlops-genaiops/llm-evaluation-production.md
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# LLM Evaluation in Production Contexts
**Kategori:** MLOps & GenAIOps
**Sist oppdatert:** 2026-06-19
**Category:** MLOps & GenAIOps
**Last updated:** 2026-06-19
**Confidence:** High (basert på offisiell Microsoft dokumentasjon, Microsoft Foundry SDK, og MLflow 3)
**Type:** reference
**Source:** https://learn.microsoft.com/azure/foundry/concepts/observability

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skills/ms-ai-engineering/references/mlops-genaiops/mlops-security-access-control.md
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# Security and Access Control in MLOps
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Last updated:** 2026-06-19
**Dato:** 2026-06-19
**Confidence:** HIGH — Basert på offisiell Microsoft Learn dokumentasjon (8 MCP-oppslag, 16 kilder)

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skills/ms-ai-engineering/references/mlops-genaiops/mlops-teams-collaboration-tools.md
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# MLOps Team Collaboration and Tools Integration
**Kategori:** MLOps & GenAIOps
**Sist oppdatert:** 2026-06-19
**Category:** MLOps & GenAIOps
**Last updated:** 2026-06-19
**Kilde:** Microsoft Learn, Azure Architecture Center
**Konfidensgradering:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)
**Type:** reference

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skills/ms-ai-engineering/references/mlops-genaiops/monitoring-observability-ml-systems.md
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# Monitoring and Observability for ML Systems
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Dato:** 2026-04
**Kilder:** Microsoft Learn (azure-machine-learning, azure-monitor)
**Konfidensgrad:** ⭐⭐⭐⭐⭐ (Verifisert mot offisiell Microsoft-dokumentasjon)

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skills/ms-ai-engineering/references/mlops-genaiops/prompt-flow-production-deployment.md
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# Prompt Flow and Production Deployment
**Kategori:** MLOps & GenAIOps
**Category:** MLOps & GenAIOps
**Dato:** 2026-02-04
**Last updated:** 2026-06-19
**Confidence:** 🟢 Høy (basert på offisiell Microsoft-dokumentasjon fra Microsoft Foundry og Azure Machine Learning)

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skills/ms-ai-engineering/references/mlops-genaiops/responsible-ai-mlops-integration.md
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# Responsible AI Integration in MLOps
**Kategori:** MLOps & GenAIOps
**Sist oppdatert:** 2026-06-19
**Category:** MLOps & GenAIOps
**Last updated:** 2026-06-19
**Confidence:** 95% (basert på offisiell Microsoft-dokumentasjon og Azure Machine Learning-referanser)
**Type:** reference
**Source:** https://learn.microsoft.com/azure/machine-learning/concept-responsible-ai-dashboard