ml-deployment-helper

# ML Deployment Helper

## Overview

Bridges the gap between trained models and production systems. Generates deployment artifacts, APIs, monitoring, and A/B testing infrastructure following MLOps best practices.

## Deployment Checklist

Before deploying any model, this skill ensures:

- ✅ Model versioned and tracked
- ✅ Dependencies documented (requirements.txt/Dockerfile)
- ✅ API endpoint created
- ✅ Input validation implemented
- ✅ Monitoring configured
- ✅ A/B testing ready
- ✅ Rollback plan documented
- ✅ Performance benchmarked

## Deployment Patterns

### Pattern 1: REST API (FastAPI)

```python
from specweave import create_model_api

# Generates production-ready API
api = create_model_api(
    model_path="models/model-v3.pkl",
    increment="0042",
    framework="fastapi"
)

# Creates:
# - api/
#   ├── main.py (FastAPI app)
#   ├── models.py (Pydantic schemas)
#   ├── predict.py (Prediction logic)
#   ├── Dockerfile
#   ├── requirements.txt
#   └── tests/
```

Generated `main.py`:
```python
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib

app = FastAPI(title="Recommendation Model API", version="0042-v3")

model = joblib.load("model-v3.pkl")

class PredictionRequest(BaseModel):
    user_id: int
    context: dict

@app.post("/predict")
async def predict(request: PredictionRequest):
    try:
        prediction = model.predict([request.dict()])
        return {
            "recommendations": prediction.tolist(),
            "model_version": "0042-v3",
            "timestamp": datetime.now()
        }
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/health")
async def health():
    return {"status": "healthy", "model_loaded": model is not None}
```

### Pattern 2: Batch Prediction

```python
from specweave import create_batch_predictor

# For offline scoring
batch_predictor = create_batch_predictor(
    model_path="models/model-v3.pkl",
    increment="0042",
    input_path=