Tutorial from ML with Django
"This tutorial provides code examples on how to build your ML system available with REST API.
In this tutorial, for building the ML service we will use Python 3.6 and Django 2.2.4.
This tutorial is the first part that covers the basics which should be enough to build your ML system which:
- can handle many API endpoints,
- each API endpoint can have several ML algorithms with different versions,
- ML code and artifacts (files with ML parameters) are stored in the code repository (git),
- supports fast deployments and continuous integration (tests for both: server and ML code),
- supports monitoring and algorithm diagnostic (support A/B tests),
- is scalable (deployed with containers),
- has a user interface."
Python 3.10, Django 4.2.5, DRF 3.14.0 [Tutorial part with docker containers is not implemented.]
For more details please visit: MLJAR