In QuickML, users have the capability to create an endpoint for their trained machine learning model. This endpoint serves as a gateway, enabling external applications to interact with the model seamlessly. By providing input data through the endpoint, external applications can receive the model’s predictions as output, making it a valuable tool for integrating machine learning models into various real-world applications.

To create an endpoint with the previously trained machine learning model, follow the steps below:

1. Access the QuickML interface and navigate to the Endpoints section.
2. Give a endpoint name and select the model that you wish to deploy as an endpoint from the list of trained models.
3. Click on the Create Endpoint option for the selected model.
4. Once the endpoint is created, you will receive a unique endpoint URL that can be used to interact with the model.