Project Overview

This contains a Machine Learning Microservice, built on Scikit-Learn. It contains a model that predicts house 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, which was initially taken from Kaggle, on the data source site.

What does this project do?

• Run docker container
• Upload container into a public registry (hub.docker.com)
• Run the deployed application in a Kubernetes cluster
• Integrate with CircleCI for continuous integration

Requirements

• Python 3.7

Steps

Step 0

• Clone repo to local machine

Step 1: Install dependencies

• Set up the environment by running make setup. This will create a virtual environment in your home directory called .devops
• Install dependencies by running make install
• (Optionally) Lint application (requires hadolint)

Step 2: Run Docker container

• Run application with command ./run_docker.sh

Step 3: Upload to Docker Hub

• In ./upload_docker.sh file, edit variable dockerpath (line 8) and change the docker username to a personalized one.
• To upload to docker hub, run ./upload_docker.sh

Step 4: Kubernetes deployment

• To deploy to kubernetes, run ./run_kubernetes.sh