When you deploy a model, it creates an endpoint for the model you want to deploy. You can send new data to this deployment/endpoint and get predictions from that model. You can achieve this with a curl command from the REST API or using our Python SDK.
Deploying your model allows you to easily apply your models to new data. You can also monitor things like service health, accuracy, and data drift using a deployment. You must have a completed model in order to deploy one. You can also deploy a model in the GUI (see the in-app documentation on Deployments for more information).