Deploying a machine learning model to production is very different from running it in a notebook. This guide covers the exact steps I used to deploy my own ML models to AWS — from containerization to auto-scaling.
Why Docker for ML Deployments?
Python dependency hell is real. Docker containers encapsulate your model, environment, and dependencies so the model that works on your laptop works identically on AWS EC2. No more "it works on my machine" excuses.
FROM python:3.11-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
EXPOSE 8000
CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]FastAPI — The Right Framework for ML APIs
FastAPI gives you async support, automatic OpenAPI docs, and typed request/response validation — perfect for ML inference endpoints that need to handle concurrent requests efficiently.
AWS Deployment Architecture
The recommended architecture for a production ML API: EC2 for compute, ECR to store Docker images, Application Load Balancer for traffic distribution, and CloudWatch for monitoring. Use CloudFormation to define this as Infrastructure as Code.
CI/CD with GitHub Actions
Automate your deployments so every merged PR triggers a build, pushes to ECR, and rolls out to EC2. This eliminates manual deployment errors and gives you reliable, repeatable releases.
"A model that isn't deployed doesn't solve any real problem."