This project operationalizes a Machine Learning Microservice API for making predictions within the Boston housing market.
A pre-trained, 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.
The project has all that's needed to:
- Setup application and environment dependencies
- Build, run, and upload docker container
- Deploy the application docker container into Kubernetes
- Define a CircleCI configuration to continuously build and lint.
app.py- Python flask app 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.Dockerfile- Docker file for building the boston house price prediction API docker container.Makefile- This defines the basic tasks used for: Setting up dependencies, building, linting, testing, ...requirements.txt- This is the python app's library dependencies installed VIA pip.run_docker.sh- The script for building the API docker image and running it.upload_docker.sh- The script used to authenticate into docker hub, and upload the API docker image.run_kubernetes.sh- The script used for deploying the API docker image into a kubernetes cluster.make_prediction.sh- The script used to make a prediction against the running API.
- Run
make first-timeto install the necessary dependencies
- Standalone:
python app.py - Run in Docker:
./run_docker.sh - Run in Kubernetes:
./run_kubernetes.sh
- Upload docker image to docker hub:
./upload_docker.sh
- Setup and Configure Docker locally
- Setup and Configure Kubernetes locally
- Create Flask app in Container
- Run via kubectl