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
- Standalone:
python app.py
- Run in Docker:
./run_docker.sh
- 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 commandMakefile
: python environment, requirement libraries installation, lint testapp.py
: flask application of the projectmodel_data
: housing prices dataset, machine learning modelmake_prediction.sh
: prediction scriptrun_docker.sh
: docker running scriptupload_docker.sh
: docker repo upload scriptrun_kubernetes.sh
: kubernetes running scriptrequirements.txt
: python library for the projectoutput_txt_files
: output prediction.circleci
: circleci configure file