Built a Continuous Delivery pipeline that deploys a Flask machine learning app using Azure Pipelines to Azure App Services. Used Github Actions and application code to perform the initial lint, test and install cycle on my app running Flask in Azure App Services.

Integrated Continuous Delivery using Azure Pipelines to deploy my tested app changes automatically to production wrapping everything up with final testing of the prediction capability of my app deployed into production

Azure Pipelines, Azure App Services, Continuous Integration and Continuous Delivery (CI/CD), Docker, Github Actions, Flask, Kubernetes, Machine Learning, MS Azure, Pandas, Pylint, Pytest, Python, Scikit-Learn.

To run the app locally you need to be running Docker. Go to the flask-sklearn directory and execute the script to start a container running the app:

$ cd flask-sklearn $ ./run_docker.sh

Open a new terminal, go to the same directory and execute the client call:

$ cd flask-sklearn $ ./make_prediction.sh

You can adjust the prediction by editing the CURL call in that script.