Machine learning Web Server

Boilerplate template to build end-end to machine learning model using scikit-learn and deploy the model as REST api using Flask

This boilerplate template provides two end points, one is create_model which takes the dataset file and the target field then build training pipeline and persist the model. The second endpoint is predict for model inference, which takes single record and produces the model prediction.

Usage

Run server locally

1. Clone the repository

   git clone https://github.com/kantharajucn/ML_web_server_boilerplate

2. change directory into the repo

   cd path/to/ML_web_server

3. Install requirements

   pip install -r requirements.txt

4. Run Flask application

   make run

5. Run tests

   make test

Run server using Docker

1. Build docker image

   make docker-build

2. Run docker image

   make docker-run

3. Run tests

   make docker-test

4. Stop docker container

   make docker-down

   Web server is available on the port 8000.