Event Grid subscription: is the most suitable choice for creating event-driven workflows in Azure.
When a training run is completed in an Azure ML workspace, Event Grid can be used to trigger a workflow automatically. This approach requires minimal administrative effort, as you can subscribe to specific events (like training run completion) and respond to them without constant polling or manual intervention.

References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/preprocess-text

Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

To control token authentication, use the token_auth_enabled parameter when you create or update a deployment Token authentication is disabled by default when you deploy to Azure Kubernetes Service.
Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:
key-based: A static key is used to authenticate to the web service.
token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service