A Simle Machine Learning Application

Project Task

The project goal is to operationalize a pre-built SKLearn Machine Learning Microservice Application using Kubernetes, which is an open-source system for automating the management of containerized applications by doing the following:

• Test project code using linting
• Create a Dockerfile to containerize this application
• Deploy the containerized application using Docker and make a prediction
• Improve the log statements in the source code for the application
• Configure Kubernetes and create a Kubernetes cluster
• Deploy the containerize app to Kubernetes cluster and make a prediction
• Use CircleCI as a CICD tool

Technology Used

Flask

pytest

PyLint

Docker

Kubernetes

CircleCI

Building from Source

You need Python 3 and a bash-like shell.

Setup the Environment

• Create a virtualenv and activate it
• 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

Verify the application is running

Application listens on port 8000

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Testing

This project uses pytest

Author

Ismail Muhammed