I am running my ML inference inside a docker container. Should I include my weights in the image, or should I download them from S3 when the container starts up? From what I can see, the benefits are as follows:

Pros for including: faster startup times since I don’t need to download after startup. Less dependencies since everything is included in the container image

Pros for downloading: separation of weights and code. Easier weight tweaking since I won’t need to redeploy image when changing weights

Thoughts?