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docs(ms-ai-architect): KB-refresh tema-b — Foundry-navnesveip «Azure AI Foundry»→«Microsoft Foundry» (233 filer)

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Verifisert mot offisiell MS-doc (juni 2026): «Microsoft Foundry» er det
gjeldende produkt-/portalnavnet; «Foundry (classic)» = gamle «Azure AI Foundry»
(/azure/foundry/ vs /azure/foundry-classic/). Premiss bekreftet før sveip.

Multi-regel, IKKE naiv s/Azure AI Foundry/Microsoft Foundry/ — MS dropper
«Azure AI» (legger IKKE til «Microsoft») for to produktvarianter:
- «Azure AI Foundry Agent[ Service|s]» → «Foundry Agent Service/Agents» (MS-form)
- «Azure AI Foundry Models» → «Foundry Models» (i «Azure OpenAI in Foundry Models»)
- «Azure AI Foundry SDK» → «Microsoft Foundry SDK» (operatør-valg)
- «Azure AI Foundry portal/project» + generisk → «Microsoft Foundry»
- Pre-eksisterende «Microsoft Foundry Models» (4) normalisert → «Foundry Models»

Bevart: «Azure OpenAI», «Azure AI Inference SDK», «Azure AI Search»,
«Azure AI Services», kode-IDer. Historisk ref «(tidligere Azure AI Foundry)»
i model-catalog-2026.md beskyttet via lookbehind. URL /azure/ai-foundry/→
/azure/foundry/ kun i owasp-llm-top10 (KB-ref); docs/-filer deferred.

Scope: skills (inkl. 3 SKILL.md) + commands + agents + README + CLAUDE.
Ekskludert: docs/ (interne), playground/+tests/ fixtures (testdata),
CHANGELOG.md (historisk logg), STATE.md (gitignored).

3 SKILL.md endret (advisor/engineering/security) → judge-cache teknisk
invalidert for disse, men scorer uendret: advisor 91, eng/gov/infra/sec 96
(alle ≥90). validate 239/0. 0 «Azure AI Foundry» igjen (utenom bevart ref).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
This commit is contained in:
Kjell Tore Guttormsen 2026-06-23 21:00:27 +02:00
commit 03d596e4ec
233 changed files with 810 additions and 810 deletions
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8
skills/ms-ai-engineering/references/mlops-genaiops/feedback-loops-continuous-improvement.md
View file

@ -76,7 +76,7 @@ mlflow.log_feedback(
**Azure-tjenester:**
- **MLflow Datasets:** Versjonert lagring av eval-datasett i Unity Catalog
- **Azure AI Foundry Agent Evaluation:** Evaluering med LLM judges (correctness, relevance, groundedness, safety)
- **Foundry Agent Evaluation:** Evaluering med LLM judges (correctness, relevance, groundedness, safety)
- **Databricks Review App:** Samle feedback fra domeneeksperter på produksjonstracer
**Best practices:**
@ -450,7 +450,7 @@ monitor_definition = MonitorDefinition(
)
```
### Azure AI Foundry (GenAI)
### Microsoft Foundry (GenAI)
**Feedback loop-komponenter:**
@ -459,7 +459,7 @@ monitor_definition = MonitorDefinition(
| **Production tracing** | MLflow Tracing (Databricks) | Span-level telemetry |
| **User feedback** | Review App | Thumbs up/down, textual feedback |
| **LLM judges** | Agent Evaluation | Automated quality scoring |
| **Monitoring dashboard** | Azure AI Foundry Observability | Quality trends, latency, errors |
| **Monitoring dashboard** | Microsoft Foundry Observability | Quality trends, latency, errors |
| **Eval datasets** | MLflow Datasets (Unity Catalog) | Versioned test sets |
| **Red teaming** | AI Red Teaming Agent | Adversarial testing for safety |
@ -715,7 +715,7 @@ mlflow.log_param("user_id_hash", user_id_hash) # Logged
1. [MLflow for GenAI Apps and Agents - Continuous Improvement Cycle](https://learn.microsoft.com/en-us/azure/databricks/mlflow3/genai/overview/) (Verified MCP 2026-04 — updated 10-step cycle; new: Trace UI for pattern identification, evaluation harness, version/prompt management tracking)
2. [Machine Learning Operations v2 - Monitoring & Feedback](https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/machine-learning-operations-v2)
3. [Generative AI App Developer Workflow - Production Monitoring](https://learn.microsoft.com/en-us/azure/databricks/generative-ai/tutorials/ai-cookbook/genai-developer-workflow)
4. [Azure AI Foundry - Observability in Generative AI](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability)
4. [Microsoft Foundry - Observability in Generative AI](https://learn.microsoft.com/en-us/azure/foundry/concepts/observability)
5. [MLOps and GenAIOps for AI Workloads - Model Maintenance](https://learn.microsoft.com/en-us/azure/well-architected/ai/mlops-genaiops#model-maintenance)
6. [AI Builder - Continuously Improve Your Model (Feedback Loop)](https://learn.microsoft.com/en-us/ai-builder/feedback-loop)