A summary of the project

The main point of the project is to operationalize a pretrained sklearn model designed to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, here. This project tests ones ability to operationalize an application. The provided file, app.py— serves out predictions (inference) about housing prices through API calls.

Instructions on how to run the Python scripts and web app.

Run this command on your command line

python3 app.py

A short explanation of the files in the repository.

• .circleci/config.yml: Contains configuration file for circleci.

• app.py: Entry point of flask application.

• docker_out.txt: Docker logs from prediction.

• Dockerfile: Docker configuration file for building docker image.

• kubernetes_out.txt: Kubernetes logs from prediction.

• make_predictions.sh: Bash script that sends data to the flask app for making predictions.

• Makefile: This is a file for automating software building procedure and other complex tasks with dependencies

• run_docker.sh: This is a bash script that automates building the docker image and running the container.

• upload_docker.sh: This is a bash script that automates uploading the image to the dockerhub and automates login as well.

• README.md: This explains the project and its structure.