ML Engineering
Model Deployment - Serving ML Models at Scale
Learn how to deploy machine learning models to production and serve them reliably at scale.
- Serving architectures - REST APIs, batch inference, edge deployment
- Scalability - handle millions of predictions per day
- Model optimization - quantization, pruning, and distillation
The goal is not to build a model, but to deploy value.
Model Deployment — Complete Guide
Deploying ML models to production requires APIs, containers, monitoring, and scalability.
Deployment Options
DfREST API Deployment
FastAPI / Flask — JSON input/output, easy to integrate. Good for most use cases.
DfDocker Container Deployment
Reproducible environment. Deploy anywhere. Scale with Kubernetes. Production standard.
DfServerless Deployment
AWS Lambda / GCP Functions — auto-scaling, pay per request. Good for sporadic traffic.
DfEdge Deployment
ONNX Runtime, TensorFlow Lite, Core ML (Apple) — low latency, works offline.
Deployment Architecture Overview
FastAPI Deployment
Example: FastAPI Model Server
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
Example: Dockerfile for ML API
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"]
Container Orchestration with Kubernetes
Key Takeaways
Summary: Model Deployment
- FastAPI is the best framework for ML APIs
- Docker ensures reproducible deployments
- Kubernetes scales to thousands of requests
- ONNX enables cross-platform deployment
- Monitor latency, errors, and data drift in production
- A/B test new models before full rollout
- Version your models for rollback capability
- Load test before production deployment
What to Learn Next
-> MLOps Master the full ML lifecycle.
-> ML System Design Design production ML systems.
-> Model Evaluation Measure and monitor model performance.
-> A/B Testing Compare model versions scientifically.
-> Model Selection Choose the best model for deployment.
-> AutoML Automate model selection and tuning.