Overview:
Example of building a regression model and deploying it with docker.
The dataset is available at https://archive.ics.uci.edu/ml/datasets/Energy+efficiency.
You should be able to run the docker image and then curl the container by sending json containing the attributes of a new building and get a json response with the heating and cooling loads predicted by your trained model.
Pull the docker image from here:
$ docker pull jbrandenburg/multioutput_rf_flask_app
Start the container:
$ docker run -d -p 5000:5000 multioutput_rf_flask_app
Verify connectivity:
$ curl http://0.0.0.0:5000/
You can submit building parameters using the following POST call:
$ curl -X POST -H "Content-Type: application/json" -d '{ "Relative Compactness": 0.74, "Surface Area": 588.00, "Wall Area": 294.00, "Roof Area": 147.00, "Overall Height": 7.00, "Orientation": 5, "Glazing Area": 0.10, "Glazing Area Distribution": 1 }' http://0.0.0.0:5000/get_estimates