MLOps Essentials: Containerizing Deep Learning Models with Docker

Why Containerize Models

Computer vision projects often depend on exact versions of Python, model weights, OpenCV, PyTorch, and system libraries. Docker gives the model a repeatable runtime, which makes development, testing, and deployment much easier.

When models like YOLO or MiDaS move from a notebook into an application, the deployment workflow matters as much as the model code.

A Clean Container Setup

A practical image should keep dependencies explicit and avoid unnecessary weight.

FROM python:3.11-slim

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

COPY . .
CMD ["python", "app.py"]

CI/CD Friendly Workflow

The deployment flow can be simple:

1. Push code to GitHub.
2. Run tests and lint checks.
3. Build a Docker image.
4. Push the image to a registry.
5. Deploy the new image to a service.

This creates a stable path from experiment to production.

MLOps Takeaway

MLOps is not only model training. It is packaging, versioning, reproducibility, monitoring, and rollback. Docker is usually the first serious step toward making AI projects operational.