Project Overview

In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API.

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.

Setup the Environment

• Create a virtualenv and activate it. It's possible to create the virtual env with make setup.
• Run make install to install the necessary dependencies

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

Files in this repo:

The project has a few shell script files to help managing the resources.

• run_docker.sh: builds a docker image and runs it, mapping the port 8000 of you computer.
• uploaddocker.sh: tags and upload the docker image to a repo in docker hub. You may change the docker hub repository to meet one of your own.
• run_kubernets.sh: deploy the application to a Kubernets node.