Containerized Deployment of MLFlow Models with Databricks Integration

• MLFlow Docker Deployment with Databricks Integration
• Architecture
• Methods
  • Used Python Libraries
  • Open Ports
• Requirements
• Instructions
  • Setting up a Databricks Service Account
  • Retrieving Databricks credentials
  • Configuration
  • Run the Code
    • Prepare the Data
    • Prediction Endpoint
  • Test if gunicorn is working

The following code utilizes gunicorn, docker, docker-compose, and mlflow to deploy models. Models are retrieved from Databricks' MLFlow registry and loaded in a dockerized gunicorn wsgi app. See Accessing MLFlow Outside of Databricks for more details.

• MLFlow
• gunicorn
• flask

• 5000

• Docker Deployment Support
• Databricks Account
• Connection to Databricks MLFlow Registry

To retrieve models from Databricks, it is highly suggested to use a service account in Databricks. See Databricks User Admin Guide for details on how to manager users.

To retrieve Databricks tokens and credentials. See Databricks-CLI Guide for more details on how to setup the databricks cli.

Please modify the values in config/web_srv.env to provide the needed credentials

Clone this repo and run the following docker-compose command to bring up the gunicorn server.

docker-compose up

Convert a pandas dataframe to json with the split format

Post a POST request to localhost:5000/predict with a json payload to predict.

In a separate terminal, run the following command to test if the gunicorn server is up. ** Note there is some startup time to connect to the MLFlow registry, so it is suggested to wait a few minutes before attempting curl the rest endpoint.

curl localhost:5000

Expected result is.

{"hello":"world"}