The backend for the Sonomaly application splits into the prediction API, model-training, and online model training.
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Sonomaly is a capstone project for the Raptor Maps Student team that was formed from the collaboration of Raptor Maps and make School. This repo is for the backend of that application that aims to be a full production level machine learning pipline that inclues continual learning/development and a model registry.
Here's why:
- Machine learning in production is a new feild and is something that data scientist need to learn
- Being able to understand industy standards for deployment to production is important
This is only one part of this project and you should look at the other parts on the team organization.
Lets get into setting this up for yourself.
Each folder in this repo has their own README.cd files that explain how they are installed and deployed. Other features, like the mongodb database and AWS S3 bucket should be setup using the usual setup instructutions given by MongoDB Client and AWS Bucker creation page.
- The API can be called from Postman, using curl, or you can use the swagger UI by going to port 8000
- The model registry is hosted using the MongoDB console site
See the open issues for a list of proposed features (and known issues).
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature) - Commit your Changes (
git commit -m 'Add some AmazingFeature') - Push to the Branch (
git push origin feature/AmazingFeature) - Open a Pull Request
Distributed under the MIT License. See LICENSE for more information.