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Deploy a ML inference service on a budget in less than 10 lines of code. BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn/Gunicorn, backend servers etc., that are simply not within the scope of a typical data scientist. BudgetML is our answer to this challenge. It is supposed to be fast, easy, and developer-friendly. It is by no means meant to be used in a full-fledged production-ready setup. It is simply a means to get a server up and running as fast as possible with the lowest costs possible.
Features
- Cloud functions are limited in memory and cost a lot at scale
- Kubernetes clusters are overkill for one single model
- Automatic FastAPI server endpoint generation (it's faster than Flask)
- Fully interactive docs via Swagger
- Built-in SSL certificate generation via LetsEncrypt and docker-swag
- Uses cheap preemtible instances but has 99% uptime!
Project Samples
