Containerizing ML Models with Docker
Multi-stage Dockerfile, CUDA base images, model artifact COPY, health endpoints.
Why Containerize ML Models
Machine learning models depend on a specific Python version, system libraries, and pinned package versions. Docker packages all of this into a single immutable image so the model runs identically on a laptop, a CI runner, and a production cluster.
For ML the biggest pain is dependency drift: a model trained against numpy 1.24 can silently misbehave on numpy 2.0. A container freezes those versions.
The Naive Single-Stage Image
A first attempt installs everything in one stage. It works, but it ships build tools (compilers, caches, dev headers) into the final image, making it large and a bigger attack surface.
FROM python:3.11
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD ["python", "serve.py"]All lessons in this course
- Containerizing ML Models with Docker
- Cloud Deployment: AWS SageMaker
- High-Performance Serving with Triton Inference Server
- Scaling and Auto-Scaling Model Endpoints