Summary

This is an Example of Opertionalizing 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 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.

Files Summary

1. Dockerfile -> specify steps to build docker image to containerize the app
2. .circleci/config.yml -> specify steps to run CI pipeline
3. app.py -> flask app
4. make_prediction.sh -> an example to test the API
5. run_docker.sh -> this will build the image and run the container on port 8080
6. run_kubernetes -> this will run the container on kubernetes cluster on port 8080
7. upload_docker.sh -> tag and upload the docker image to docker hub
8. Makefile -> contains commands to test/install/lint.
9. requirmenets.txt -> contains dependencies of the API.

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