Machine Learning Model Deployment with Docker and Kubernetes

Deploying ML models to production requires careful consideration of scalability, monitoring, and versioning. This guide walks through a production-ready deployment pipeline.

# Dockerfile for ML model serving
FROM python:3.12-slim

WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

# Health check endpoint
HEALTHCHECK --interval=30s --timeout=10s 
  CMD curl -f http://localhost:8000/health || exit 1

CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

from fastapi import FastAPI
from pydantic import BaseModel
import mlflow
import numpy as np

app = FastAPI()

class PredictionRequest(BaseModel):
    features: list[float]
    model_version: str = "latest"

class PredictionResponse(BaseModel):
    prediction: float
    confidence: float
    model_version: str

@app.post("/predict", response_model=PredictionResponse)
async def predict(request: PredictionRequest):
    model = mlflow.pyfunc.load_model(
        f"models:/production-model/{request.model_version}"
    )
    features = np.array([request.features])
    prediction = model.predict(features)

    return PredictionResponse(
        prediction=float(prediction[0]),
        confidence=0.95,
        model_version=request.model_version,
    )