Navigating the ML deployment interface

When you open your ML deployment, you can manage its approval status and use it to create predictions on datasets.

Open an ML deployment from the catalog. There are navigation options in the user interface for model approval, deployment information, dataset predictions, and real-time predictions.

Model approval status

Before the ML deployment can generate predictions, its source model needs to be activated. This process is known as model approval, and helps to control the number of actively used deployed models in the subscription.

If you have the correct permissions, you can activate and deactivate the source model as needed. Otherwise, contact a tenant administrator or other user with sufficient permissions.

See:

• Approving deployed models

• Working with model approval as an administrator

Deployment overview

The Deployment overview shows the features used in the model training and details for the deployment.

Dataset predictions

Dataset predictions displays an overview of the prediction configurations of the ML deployment. You can have several prediction configurations for an ML deployment.

You can use the Actions menu to run, edit, or delete predictions. You can also edit and delete prediction schedules from this menu.

If no schedule is currently configured for your prediction, you can also use the Actions menu to create a new prediction schedule.

If you select Edit prediction configuration, the Prediction configuration pane is opened.

Real-time predictions

The Real-time predictions pane gives you access to the real-time prediction API. If the model in the ML deployment is activated for making predictions, this pane is visible.

For information about the prediction API, see Creating real-time predictions.

View ML experiment

Click View ML experiment in the bottom left corner of the page to open the ML experiment from which the ML deployment was created.