just stumbled onto this piece about the gap between running cells in jupyter and building real systems. it hits on how much of a
total mindset shift you need when moving past simple experimentation. most people think it is just about wrapping an api, but the architecture needs to be fundamentally different to handle production loads. i always thought
deployment was the easy part but seeing the emphasis on engineering discipline makes me rethink my current workflow. the article argues that you cannot just rely on
basic wrappers and expect stability in a live environment. it is less about the model itself and more about the underlying infrastructure and how you manage the lifecycle of the system. i am curious if anyone else has struggled with moving from a local prototype to a scalable service without everything breaking. does anyone have a specific
docker-compose
setup or pipeline they recommend for this transition? it is definitely not as simple as
just hitting run on a notebook.
more here:
https://thenewstack.io/notebook-to-production-ai/