Maintenance

❗ This project is released as-is, and is not actively maintained by Civis Analytics.

CivisML Model Deployment

This repository contains the source code for a python Flask app that enables users to deploy machine learning models built in CivisML⁠. This Flask app is built in the container specified by the Dockerfile, and the container is deployed in its own Kubernetes pod hosted on Civis Platform. A Jupyter notebook demonstrating how to train, deploy, and make requests of a deployed CivisML model can be found in the examples/ directory.

The version of CivisML that was used to train your model dictates which version of the model-deployment Docker image you should use when you deploy. The easiest way to check which model-deployment image you should use is to check which version of the Civis python API client your model was trained with. The below table tells you which image tag to use for a given version of the Civis python API client.

Resources of the Deployed Model

Once deployed, you will be able to make HTTP GET requests to three resources. The "/predict" endpoint is the one model consumers will be most concerned with. Requests for predictions can be made by passing the values of your model's covariates as query parameters to this endpoint. You can pass your access token either through the HTTP header, or your can pass it as a query parameter using the "civis_service_token" variable name. An example GET to "/predict" is constructed in the example notebook.

A GET call to the index page "/" will return a web form that will allow you to enter values for your covariates into a GUI to get predictions. This form is primarily meant to be consumed through Civis Platform by using the "Publish to Report" option in your deployment's page on Platform. There is a "/features" endpoint that returns the features of your deployed model in JSON. This is primarily meant to populate the web form returned by GET calls to the index.

Deploying non-CivisML Models

This repo primarily focuses on the use case of deploying models trained in CivisML. In spite of that, we have written a development branch called "muffnn_recommendation_engine" that shows how to deploy a simple recommendation engine. The model uses the muffnn⁠ library, which makes picklable TensorFlow models that follow the scikit-learn API⁠. While users can certainly deploy a muffnn model using this code, it is also meant as an illustration of the changes one would need to make to repurpose this repo for non-CivisML models.