kubernetes-microservices

Containerized micro-services for automated Machine Learning and Prediction

Project Overview

The Kubernetes-Microservices project contains a Machine Learning Microservice, built using 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.

Features of this project

• Installs necessary python binary
• Lints the python files
• Lints the Docker file
• Runs a 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
• (Optional): It is advisable to use a cloud environmentsuch as AWS cloud9 to avoid installation issues.

Step 1

• Start by forking this repository

Step 2: 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
• Install hadolint if you want to lint the Docker file
• (Optionally) Lint application (requires hadolint)
• Lint the necessary files by running make lint

Step 2: Run Docker container

• Run the application on docker using the bash script ./run_docker.sh

Step 3: Upload to Docker Hub

• In the ./upload_docker.sh bash script, edit the dockerpath 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

Happy coding! Your's sincerely, Olufunbi!