This is a Web Service that uses a pretrained model to predict Boston house prices. The web service is deployed to Azure as an App service.
- Trello: https://trello.com/b/DglsTOf3/udacity-ci-cd
- Spred Sheet: https://docs.google.com/spreadsheets/d/1RVkXWyg3vh1xjo7_pgdA8kCZQgEEg9pqjFrYXtfy8wY/edit?usp=sharing
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.
- Project running on Azure App Service
- Project cloned into Azure Cloud Shell
- GitHub Actions
- Passing tests that are displayed after running the
make all
command from theMakefile
- Output of a test run
- Successful run of the project in Azure Pipelines
- Running Azure App Service
- Successful prediction from deployed flask app in Azure Cloud Shell>
The most obvious enhancement is to add a Web GUI for the application to allow users to make predictions interactively.
After that adding support for more cities is the most obvious next step.