This project is all about operationalizing a Machine Learning Microservice API app.py—that serves out predictions (inference) about housing prices through API calls.

Using 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. The Data set is gotten from the data source site.

• Run make setup to setup python virtual environment. Just like below

python3 -m venv ~/.devops
# You should have Python 3.7 available in your host. 
# Check the Python path using `which python3`
source .devops/bin/activate

• Run make install to install the necessary dependencies
• Install hadolint with these commands wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 && chmod +x /bin/hadolint. This will help lint Dockerfile.
• Install docker using this link.
• Install minikube using this link. This will help to run Kubernetes locally.

• Run make lint to check the source and Dockerfile

• Run python3 app.py

• Run ./run_docker.sh then run ./make_predicton.sh to make predictions.
• Run ./upload_docker.sh to upload the docker image to docker hub.

3. Run ./run_kubernetes.sh then run ./make_predicton.sh to make predictions.

.
├── Dockerfile
├── Makefile
├── README.md
├── app.py
├── hadolint
├── make_prediction.sh
├── model_data
│   ├── boston_housing_prediction.joblib
│   └── housing.csv
├── output_txt_files
│   ├── docker_out.txt
│   └── kubernetes_out.txt
├── requirements.txt
├── run_docker.sh
├── run_kubernetes.sh
└── upload_docker.sh

• .circleci/config.yml contains the config file for cirleci for CI integration.
• output_txt_files contains the prediction outputs for docker run and kubernates run after make_prediction.sh script was run.
• app.py contains the source code of the application
• Dockerfile contains the configuration for the docker container env. setup
• Makefile basically contains the summary instruction on deploying the app
• requirements.txt contains the dependencies to install