Containerize the Model — Docker & a Private Registry
Stop saying “it works on my machine” about four gigabytes of model weights.
# what you build
A model is code, plus weights, plus a very specific Python environment — which is exactly the problem containers solve. You will write a Dockerfile for the inference service, dodge the traps that make AI images ten gigabytes, and push to a private registry on all three clouds so the same image runs anywhere without a rebuild.
What you end up with
handles after this
~50 people at once
what it costs to run
$10–20 / month
What you will be able to do
- Write a Dockerfile for a model service that is not ten gigabytes
- Decide where weights belong — baked into the image, or mounted at start
- Multi-stage builds and layer caching, so a slow Python install happens once
- Push to a private registry on AWS, Azure and DigitalOcean
- Run the identical image on any of the three clouds, unchanged
The build, step by step
These are the chapters of the video, in order.
- 1Write the DockerfileA slim base, pinned dependencies, and a non-root user.
- 2Shrink the imageMulti-stage build, cached layers, and the model weights handled deliberately.
- 3Push to three registriesECR, ACR and DigitalOcean Container Registry — one image, three homes.
- 4Run it anywherePull and run the same tag on each cloud, and prove the behaviour is identical.
The stack
- Docker
- ECR
- ACR
- DO Registry
- Python
# before this one
This project continues the system built in 01 · Your First AI API on One Cloud Server. You can start here, but the repo assumes the previous rung exists.
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