Overview

In this project, I containerized and deployed a machine learning application using Kubernetes a operationalize a Machine Learning Microservice API.

Having a given 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.

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How to run this project.

• Create a virtualenv and activate it
• Run make install to install the necessary dependencies
• Run run_docker.sh: Build the docker image from the Dockerfile; and tags it. List the created docker images (for logging purposes). Run the containerized Flask app; publish the container’s port 80 to a host on port 8000
• Run the make_prediction.sh file to make predictions.

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 =======