Project Overview

In order to operationalize a Machine Learning Microservice API for this project, I used the skills I learned in the course.

You are given a pre-trained, sklearn model that has been trained 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, which was initially taken from Kaggle, on the data source site. This project tests your ability to operationalize a Python flask app—in a provided file, app.py—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.

Purpose

• Invoke a Docker container
• Place the container in a public registry (hub.docker.com)
• Utilize a Kubernetes cluster to run the deployed application.
• Use CircleCI to integrate for continuous integration.

Dependencies

• Python 3.7
• Hadolint
• Docker
• Kubernetes(minikube)

Usage and files

• Most things are terminal commands and
• long commands has been moved to shell script file whith .sh extension.
• Examples:
  • run_docker.sh
  • make_prediction.sh which contain a curl command that can make a POST request to local instance of the app send along json data to be used for the prediction
  • Makefile with no extension, is a special file, containing shell commands.
• clone the repo: git clone [email protected]:ezehlivinus/udapeople-project4-demo.git
• navigate to the project directory: cd git clone udapeople-project4-demo
• setup the environment:
  • On your terminal run: make setup
• Install dependencies using make by running the following on your terminal
  • make setup
• You may lint: make lint
• Run the application using docker: ./run_docker.sh

Uploading to your docker hub account

You can do this with the script file ./upload_docker.sh. we make an environment to keep our username and desired image.

• Locate the following line of code in the script: dockerpath=ezehlivinus/udapeople
• Chnage ezehlivinus to your docker hub username
• Change udapeople to your prefered image name
• In essence: the code syntax is: dockerpath=your-docker-hub-username/image-name
• If you changed the user and the image name
  • You may want to do the same by changing the same code on this file: ./run_kubernetes.sh as you will be using next

Deployment

The script file ./run_kubernetes.sh contain the code run the deployment to kubernetes

• Note: deploy as your own project,
• Run the following command to deploy: ./run_kubernetes.sh