/operationalize-ml

Primary LanguagePython

kcemenike

Project Overview

The Operationalize ML project 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 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

START HERE

Step 0

  • Fork this repo and clone it to your local workstation (obviously)

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 the application on docker by calling ./run_docker.sh

Step 3: Upload to Docker Hub

  • In the ./upload_docker.sh file, edit the dockerpath (line 8) and change the docker username to a personalized one (or leave it as is, to use the public kcemenike/microproject:v1.0.0)
  • To upload to docker hub, run ./upload_docker.sh

Step 4: Kubernetes deployment

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