The purpose behind this project was to demonstrate how to build an instant machine learning application with Streamlit - this is great for rapid prototyping. To achieve this I created a simple classification model on the Car Evaluation Dataset from the UCI Machine Learning Repository. By following along with the articles below, you will learn how to: create a machine learning microservice, create a front end for your machine learning model, and how to wire the two applications together using Docker and Docker-compose. The GIF below is a demonstration of how the application works.
These instructions assume that you already have Docker and Docker-compose installed on your machine - if not, please follow the instructions here.
- Clone this repository to your computer
- Navigate to the root of the project:
cd car-evaluation-project - Build the docker images using
docker-compose up -d --build- This may take a minute
- Open your browser and navigate to http://localhost:8501 to use the application.
- Conduct analysis of the data to build a better classification model
- Set up monitoring for the machine learning model
- Deploy on the cloud