> Deploying Machine Learning Models to Production

Moving machine learning models from research to production requires careful consideration of deployment architecture, monitoring, and maintenance strategies.

Model Serving Architecture

A production-ready ML serving system needs to handle versioning, A/B testing, and graceful fallbacks:

from fastapi import FastAPI
from pydantic import BaseModel
import mlflow.pyfunc

app = FastAPI()

# Load model from MLflow
model = mlflow.pyfunc.load_model("models:/production-model/latest")

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

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

Monitoring and Observability

Track model performance, data drift, and system metrics in production. Set up alerts for degradation in prediction quality or system health.