Delete an online prediction model

Preview: Online Prediction is a Preview feature that is available as-is and is not recommended for production environments. Google provides no service-level agreements (SLA) or technical support commitments for Preview features. For more information, see GDC's feature stages.

This page describes the process to delete an online prediction model and all the resources associated with it.

Before you begin

To get the permissions that you need to access Online Prediction, ask your Project IAM Admin to grant you the Vertex AI Prediction User (vertex-ai-prediction-user) role.

For information about this role, see Prepare IAM permissions.

Additionally, to get the permissions that you need to delete objects in a bucket, ask your Project IAM Admin to grant you the Project Bucket Object Admin (project-bucket-object-admin) role in the project.

Delete resources

If you want to delete an online prediction model and all the resources associated with it, perform the following steps:

1. Delete the DeployedModel custom resource associated with your model on the prediction cluster:

   kubectl --kubeconfig PREDICTION_CLUSTER_KUBECONFIG delete -f DEPLOYED_MODEL_NAME.yaml
   

   Replace the following:

   • PREDICTION_CLUSTER_KUBECONFIG: the path to the kubeconfig file in the prediction cluster.
   • DEPLOYED_MODEL_NAME: the name of the DeployedModel definition file.

2. Edit the Endpoint custom resource in one of the following ways:

   • If the endpoint that the DeployedModel uses doesn't host other models, delete the Endpoint custom resource on the prediction cluster:

     kubectl --kubeconfig PREDICTION_CLUSTER_KUBECONFIG delete -f ENDPOINT_NAME.yaml
     

     Replace ENDPOINT_NAME with the name of the Endpoint definition file.

   • If the endpoint that the DeployedModel uses hosts other models, perform the following steps:

     1. Update the Endpoint custom resource on the prediction cluster:

        kubectl --kubeconfig PREDICTION_CLUSTER_KUBECONFIG edit -f ENDPOINT_NAME.yaml
        

        Replace ENDPOINT_NAME with the name of the Endpoint definition file.

     2. On the YAML file, manually delete the serviceRef object containing the DeployedModel reference you deleted previously.

     3. Save the changes on the YAML file.

3. Delete your model from the storage bucket. For more information about how to delete objects from storage buckets, see Delete storage objects in projects.