This repository demonstrates the deployment of MLflow models by leveraging compute sharing on a model serving endpoint. By sharing compute resources, you can optimize costs and reduce the total cost of ownership for your ML deployments.
To get started, add this notebook to your Databricks workspace. You can also start with this example that trains two models and then deploy those to a single endpoint