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
make setup
source ~/.devops/bin/activate
- Run
make install
to install the necessary dependencies
- Standalone:
python app.py
- Run in Docker:
./run_docker.sh
- Make a prediction with
./make_prediction.sh
minikube start
./run_kubernetes.sh
- Make a prediction with
./make_prediction.sh