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commands/specweave-ml-deploy.md

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+---
+name: specweave-ml:ml-deploy
+description: Generate deployment artifacts (API, Docker, monitoring)
+---
+
+# Deploy ML Model
+
+You are preparing an ML model for production deployment. Generate all necessary deployment artifacts following MLOps best practices.
+
+## Your Task
+
+1. **Generate API**: FastAPI endpoint for model serving
+2. **Containerize**: Dockerfile for model deployment
+3. **Setup Monitoring**: Prometheus/Grafana configuration
+4. **Create A/B Test**: Traffic splitting infrastructure
+5. **Document Deployment**: Deployment runbook
+
+## Deployment Steps
+
+### Step 1: Generate FastAPI App
+
+```python
+from specweave import create_model_api
+
+api = create_model_api(
+    model_path="models/model.pkl",
+    framework="fastapi"
+)
+```
+
+Creates: `api/main.py`, `api/models.py`, `api/predict.py`
+
+### Step 2: Create Dockerfile
+
+```python
+dockerfile = containerize_model(
+    model_path="models/model.pkl",
+    python_version="3.10"
+)
+```
+
+Creates: `Dockerfile`, `requirements.txt`
+
+### Step 3: Setup Monitoring
+
+```python
+monitoring = setup_monitoring(
+    model_name="recommendation-model",
+    metrics=["latency", "throughput", "error_rate", "drift"]
+)
+```
+
+Creates: `monitoring/prometheus.yaml`, `monitoring/grafana-dashboard.json`
+
+### Step 4: A/B Testing Infrastructure
+
+```python
+ab_test = create_ab_test(
+    control_model="model-v2.pkl",
+    treatment_model="model-v3.pkl",
+    traffic_split=0.1
+)
+```
+
+Creates: `ab-test/router.py`, `ab-test/metrics.py`
+
+### Step 5: Load Testing
+
+```python
+load_test_results = load_test_model(
+    api_url="http://localhost:8000/predict",
+    target_rps=100,
+    duration=60
+)
+```
+
+Creates: `load-tests/results.md`
+
+### Step 6: Deployment Runbook
+
+Create `DEPLOYMENT.md`:
+
+```markdown
+# Deployment Runbook
+
+## Pre-Deployment Checklist
+- [ ] Model versioned
+- [ ] API tested locally
+- [ ] Load testing passed
+- [ ] Monitoring configured
+- [ ] Rollback plan documented
+
+## Deployment Steps
+1. Build Docker image
+2. Push to registry
+3. Deploy to staging
+4. Validate staging
+5. Deploy to production (1% traffic)
+6. Monitor for 24 hours
+7. Ramp to 100% if stable
+
+## Rollback Procedure
+[Steps to rollback to previous version]
+
+## Monitoring
+[Grafana dashboard URL]
+[Key metrics to watch]
+```
+
+## Output
+
+Report:
+- All deployment artifacts generated
+- Load test results (can it handle target RPS?)
+- Deployment recommendation (ready/not ready)
+- Next steps for deployment