Machine Learning microservice with FastAPI

App to interact with machine learning models trained on the Titanic dataset.

There are 2 versions (/v1 and /v2) each with two API endpoints: /predict and /score.

/v1 features a regularized logistic model. /v2 features a random forest model. /predict returns the predicted probability of survival. /score returns some test set metrics of the model (ie ROC AUC, accuracy and recall).

Live demo

The app is currently (and hopefully) live on AWS ECS (Amazon Elastic Container Service)

http://fastapi-demo-nlb-ec2-cc72dbfaa6e8866d.elb.eu-west-1.amazonaws.com

Usage

Build and spin up the app locally using Docker

$ git clone https://github.com/fedassembly/fastapi-demo.git
$ cd fastapi-demo
$ make venv  # works on MacOS only
$ docker-compose up

Roadmap

• Add unit tests to titanic_model and api_utils
• Add CI/CD