Performing machine learing task with flask and Azure Devops; everything is running to serverless
- Launch Cloud shell
- Set up the GitHub Repo and integrate Azure Pipelines from the GutHub Marketplace
- Clone this repository in the enviroment
cdinto the directory
cd flask-ml-AzureDevOps-serverlss
- Set up a virtual enviroment
python3 -m venv ~/.flask-ml-AzureDevOps
source ~/.flask-ml-AzureDevOps/bin/activate
- Run the
make installcommand to install the dependencies from themakefile. - Create an app service and deploy as well then verify
az webapp up -n <your-appservice>; choose any name for your appservice. Verify by running the link in your browser
https://<your-appservice>.azurewebsites.net/
- Perform prediction by changing the
make_predict_azure_app.shfile to match the app service URL - Create an Azure DevOps Project using this documentation
- Set up a Continuous Delivery Workflow following this documentation as well. The good thing is the template has it all figured out for your application.
- Check your build and confirm it's working, probably get a status badge and update your READme
All the steps mentioned are just to perform a simple deployment with the Azure Pipeline and a demo project. The steps does not vary with other types of applications; the goal of the repo is to explore Azure Pipelines not neccesarily the application type.
Looking for an engineer to build and automate your next application infrastruture/architecture to work remotely? Get in touch: sbayo971@gmail.com
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