Project Overview

This is a udacity DevOps project.

In this project, a Machine Learning Microservice API is operationalized

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 I 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.

Project Files

The project goal is to operationalize this working, machine learning microservice using kubernetes, which is an open-source system for automating the management of containerized applications.

• Dockerfile: using to build docker image
• Makefile: includes instructions on environment setup and lint tests
• requirements.txt: a file to install dependencies
• run_docker.sh: To run and build a docker image
• app.py: serves out predictions (inference) about housing prices through API calls
• make_prediction.sh: sending some input data to your containerized application via the appropriate port
• run_kubernetes.sh: This script should create a running pod
• upload_docker.sh: upload built image to docker
• .circleci/config.yml: calling to identify how you want your testing environment set up and what tests you want to run.
• output_txt_files: log statements

How to run

• clone the project repository:
  • git clone https://github.com/hamaer0214/udacitykube.git
• To run and build a docker image
  • bash run_docker.sh:
• Then, to make a prediction, open another terminal windown and run,
  • bash make_prediction.sh
• To use Kubernetes to run docker image
  • minikube start
  • bash run_kubernetes.sh