mbiombani/microservice

Project Overview

In this project we had to operationalize a Machine Learning Microservice API which uses 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.

Setup the Environment

• Run git clone git@github.com:mbiombani/microservice.git
• Run cd microservice
• Run python3 -m venv ~/.devops
• Run source ~/.devops/bin/activate
• Run make install to install the necessary dependencies

Installing Docker

• Create docker account
• Install the lastest [stable release]
• Run docker --version

Installing Kubernetes

Mac

• Run brew cask install virtualbox to install VirtualBox
• Run brew cask install minikube to install minikube

Windows

• Install VirtualBox
• Install minikube

Configure Kubernetes to Run Locally

• run minikube start

Running app.py

1. Standalone: python app.py
2. Run in Docker: ./run_docker.sh
3. Run in Kubernetes: ./run_kubernetes.sh

Kubernetes Steps

• Setup and Configure Docker locally
• Setup and Configure Kubernetes locally
• Create Flask app in Container
• Run via kubectl