2nd/10_Wiki/Topics/AI_and_ML/다수 팀 협업 환경.md

---
id: wiki-2026-0508-다수-팀-협업-환경
title: 다수 팀 협업 환경
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Multi-team Collaboration, Multi-Team AI Workflow, Cross-team AI Coordination]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [collaboration, multi-team, ai-workflow, ml-platform]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: mlflow-langfuse-argo
---

# 다수 팀 협업 환경

## 매 한 줄
> **"매 multi-team AI 환경은 shared model registry + isolated namespace + cross-team observability 의 triad"**. 2024년 단일 ML team 시대가 끝나고, 2026년 enterprise 의 매 product team 마다 LLM/agent 를 owning. Shared infra (model registry, eval harness, prompt store) 위에 team-isolated workspace (Argo namespace, Langfuse project) 를 결합.

## 매 핵심

### 매 conflict 영역
- **model versioning**: team A 가 fine-tune 한 Llama-3.3 70B 를 team B 도 쓰는데 update 시 regression.
- **prompt drift**: 동일 base prompt 가 team 마다 fork 되어 7가지 variant 공존.
- **eval inconsistency**: team 마다 다른 eval set → 비교 불가.
- **GPU contention**: H200 cluster 의 fair-share scheduling 부재 시 noisy neighbor.

### 매 governance layer
- **Model Registry (MLflow / Weights & Biases)**: canonical model card, semver tag, deprecation policy.
- **Prompt Store (Langfuse / Humanloop)**: versioned prompts, A/B winner promotion.
- **Eval Harness (Inspect AI / promptfoo)**: shared regression suite — 매 model bump 시 자동 trigger.
- **Observability (Langfuse + OpenTelemetry)**: 매 team project 분리, leadership level 의 cross-team dashboard.

### 매 응용
1. Platform team 이 base infrastructure 제공, product team 이 application layer 구축.
2. AI Center of Excellence — 매 quarterly review of cross-team incidents.
3. RACI matrix — model owner / prompt owner / eval owner 명시.

## 💻 패턴

### MLflow shared registry — team isolation via aliases
```python
import mlflow
from mlflow import MlflowClient

client = MlflowClient(tracking_uri="https://mlflow.corp/")

# Platform team registers canonical model
mv = client.create_model_version(
    name="llama-3.3-70b-instruct-finetuned",
    source="s3://corp-models/llama33-v4/",
    description="Q2 2026 finetune; eval set v3.2",
)
client.set_registered_model_alias(
    name="llama-3.3-70b-instruct-finetuned",
    alias="prod-team-search",
    version=mv.version,
)
client.set_registered_model_alias(
    name="llama-3.3-70b-instruct-finetuned",
    alias="prod-team-support",
    version=mv.version,
)
# Each team pins its own alias → independent rollout cadence
```

### Langfuse multi-project prompt versioning
```python
from langfuse import Langfuse

lf = Langfuse(public_key=PK, secret_key=SK, host="https://langfuse.corp")

# Team A creates a prompt
lf.create_prompt(
    name="search/intent-classifier",
    prompt="You classify user search intent. Categories: {{categories}}.",
    labels=["production"],  # auto-promoted version label
    config={"model": "claude-opus-4-7", "temperature": 0.0},
)

# Team B compiles the same prompt (linked, not copied)
prompt = lf.get_prompt("search/intent-classifier", label="production")
compiled = prompt.compile(categories="navigational, informational, transactional")
```

### Argo Workflows — team-namespaced GPU jobs with priority class
```yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
  generateName: finetune-team-search-
  namespace: ai-team-search
spec:
  entrypoint: train
  podGC: { strategy: OnWorkflowSuccess }
  templates:
    - name: train
      priorityClassName: team-search-prod  # fair-share scheduling
      container:
        image: corp-registry/finetune:cuda12.6
        resources:
          limits: { nvidia.com/gpu: 8, memory: 1Ti }
        env:
          - name: MLFLOW_TRACKING_URI
            value: https://mlflow.corp
          - name: WANDB_PROJECT
            value: team-search
```

### Cross-team eval harness with Inspect AI
```python
from inspect_ai import eval_async, Task, task
from inspect_ai.dataset import json_dataset
from inspect_ai.scorer import model_graded_qa

@task
def shared_safety_suite():
    return Task(
        dataset=json_dataset("s3://corp-evals/safety-v3.2.jsonl"),
        scorer=model_graded_qa(model="claude-opus-4-7"),
    )

# Run across all team-owned models nightly
models = [
    "team-search/llama-3.3-70b@prod",
    "team-support/llama-3.3-70b@prod",
    "team-rec/llama-3.3-70b@prod",
]
results = await eval_async(shared_safety_suite, model=models)
# Publish to shared dashboard; alert if any team regresses >2% vs last week
```

### OPA policy gate for model promotion
```rego
package modelregistry.promotion

deny[msg] {
    input.action == "promote"
    input.target_alias == "prod"
    not input.eval_results.safety_pass_rate >= 0.95
    msg := sprintf("safety_pass_rate=%.3f below 0.95", [input.eval_results.safety_pass_rate])
}

deny[msg] {
    input.action == "promote"
    not input.has_owner_approval
    msg := "missing model_owner approval"
}
```

### Cross-team incident postmortem template (YAML, version-controlled)
```yaml
incident_id: 2026-Q2-013
date: 2026-05-08
owning_team: team-search
affected_teams: [team-support, team-rec]
root_cause: |
  team-search rolled new finetune to alias=prod without notifying
  downstream consumers; intent-classifier prompt assumed older format.
detection: Langfuse anomaly (latency p95 spike) — 14 min
resolution: rolled back model alias; published deprecation policy
action_items:
  - owner: platform-team
    due: 2026-05-22
    task: enforce 7-day deprecation notice via OPA
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| 2-3 teams, single product | shared monorepo + single MLflow project |
| 5-15 teams, mixed maturity | platform team + per-team namespace |
| 15+ teams, enterprise | full governance layer + AI CoE + OPA gates |
| Regulated (finance/health) | add audit log + immutable model lineage |

**기본값**: MLflow registry + Langfuse prompt store + Argo namespace per team + shared Inspect AI eval suite.

## 🔗 Graph
- 응용: [[Model_Registry]] · [[Large_Frontend_Projects]]
- Adjacent: [[Iterative Prompting]] · [[Parameter]]

## 🤖 LLM 활용
**언제**: 매 enterprise 의 5+ teams 가 LLM/agent product 를 ship 할 때, shared eval/registry 가 미존재할 때.
**언제 X**: 매 single team / single model — 매 over-engineering. Notion + GitHub 면 충분.

## ❌ 안티패턴
- **Shadow IT model**: team 이 platform 우회하여 personal HF token 으로 model serving — security/cost blind spot.
- **Prompt copy-paste**: Slack 으로 prompt 공유 → drift, no versioning.
- **Eval set fragmentation**: team 마다 자체 eval → cross-team comparison 불가.
- **No deprecation policy**: alias=prod 의 silent breaking change.
- **Single GPU pool, no priority class**: noisy neighbor 가 매 production inference 를 starvation.

## 🧪 검증 / 중복
- Verified (MLflow 2.x docs, Langfuse v3 multi-project, Argo Workflows fair-share scheduling, Inspect AI 0.3+).
- 신뢰도 A — 매 production-grade enterprise pattern.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — multi-team AI governance triad + 6 patterns |