What is this?

This repo´s only purpose is to demonstrate how to containerize a simple project with dockeror kubernetes.

It is made as part of the curriculum of Udacity's Cloud DevOps Engineer Nanodegree Program, project Operationalize a Machine Learning Microservice API. This work is based on Udacity's starter code.
In this repo a pre-trained, sklearn model is included 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.

Installation

Setup the Environment (for native use)

• Create a virtualenv and activate it
• Run make install to install the necessary dependencies

Linux, Mac and Windows 10 WSL:

$ python -m venv venv
$ source venv/bin/activate
(venv)$ make install

Usage

Using with docker

$ ./run_docker.sh

Using with kubernetes

$ ./run_kubernetes.sh

Make a prediction

Either run make_prediction.sh or send a http POST request to the the respective endpoint, http://localhost:80/predict. Study the make_prediction.sh file as to how the payload should look like.