Project Overview

This project operationalize a Machine Learning Microservice API.

The sklearn model 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 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.

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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 linting: make lint
3. Run in Docker: ./run_docker.sh
4. Upload image to DockerHub: ./upload_docker.sh <dockerhub-username>
5. 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