Operationalize-a-Machine-Learning-Microservice-API

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.

• Create a virtualenv and activate it
• Run make install to install the necessary dependencies

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

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

• Dockerfile : docker image, requirement libraries, running command
• Makefile : python environment, requirement libraries installation, lint test
• app.py : flask application of the project
• model_data : housing prices dataset, machine learning model
• make_prediction.sh : prediction script
• run_docker.sh : docker running script
• upload_docker.sh : docker repo upload script
• run_kubernetes.sh : kubernetes running script
• requirements.txt : python library for the project
• output_txt_files : output prediction
• .circleci : circleci configure file