this is when it dawned on me that the roles in the AI developer world are fractured into two. Data Scientists and AI devs want easy notebooks to test methods and techniques but do not care to ship something that can be easily be consumed by applications.
In the other camp lies application devs, they just want simple API's that they can use to test quickly and verify these AI methods enhance their application.
Enter KitchenAI.
A way to bridge the gap between the two by converting AI related Jupyter notebooks into a ready made production API server so that it becomes easy to test various cookbooks, recipes, and techniques. Shortening the development cycle in half while giving users a complete local experience with the ability to share them as docker containers.
Completely vendor agnostic and framework agnostic, the goal is to give developers the most about of freedom to use the libraries they already feel most comfortable using.
It comes with a plugin architecture so I envision our team and the community building all sorts of llmops type plugins like evaluation frameworks, observability, prompt management and more.
A lot of hard work was put to provide something that is totally open source, local, and with battle tested technology like Django so that developers didn't have to rely on 3rd party providers.
We’ve launched this repo under Apache license so any developer can use the tool. We're working hard to provide a managed cloud version with much deeper integrations, metrics, analytics, and workflows for those that want have more complex demands
Give it a spin: https://github.com/epuerta9/kitchenai. Let us know what you think!