Model Deployment — Complete Guide

Deploying ML models to production requires APIs, containers, monitoring, and scalability.

Deployment Options

REST API:
├─ FastAPI / Flask
├─ JSON input/output
├─ Easy to integrate
└─ Good for most use cases

Docker Container:
├─ Reproducible environment
├─ Deploy anywhere
├─ Scale with Kubernetes
└─ Production standard

Serverless:
├─ AWS Lambda / GCP Functions
├─ Auto-scaling
├─ Pay per request
└─ Good for sporadic traffic

Edge:
├─ ONNX Runtime
├─ TensorFlow Lite
├─ Core ML (Apple)
└─ Low latency, offline

FastAPI Deployment

from fastapi import FastAPI
from pydantic import BaseModel
import joblib

app = FastAPI()
model = joblib.load("model.pkl")

class PredictionRequest(BaseModel):
    features: list[float]

@app.post("/predict")
def predict(request: PredictionRequest):
    prediction = model.predict([request.features])
    return {"prediction": prediction[0]}

Docker

FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

Key Takeaways

1. FastAPI is the best framework for ML APIs
2. Docker ensures reproducible deployments
3. Kubernetes scales to thousands of requests
4. ONNX enables cross-platform deployment
5. Monitor latency, errors, and data drift in production
6. A/B test new models before full rollout
7. Version your models for rollback capability
8. Load test before production deployment