A. Create a Snowflake Task that periodically executes a SQL query to calculate performance metrics (e.g., RMSE) by comparing predicted values from the external function with actual values stored in a separate table. Trigger a Python UDF, deployed as a Snowflake stored procedure, to initiate retraining if the RMSE exceeds the threshold.

B. Implement custom logging within the AWS Lambda function to capture prediction results and actual values. Configure AWS CloudWatch to monitor these logs and trigger an AWS Step Function that initiates a new training job and updates the Snowflake external function with the new model endpoint upon completion.

C. Utilize Snowflake's Alerting feature, setting an alert rule based on the output of a SQL query that calculates performance metrics. Configure the alert action to invoke a webhook that triggers a retraining pipeline.

D. Build a Snowpark Python application deployed on Snowflake which periodically polls the external function's performance by querying the function with a sample data set and comparing results to ground truth stored in Snowflake. Initiate retraining directly from the Snowpark application if performance degrades.

E. Create a view that joins the input features with the predicted output and the actual result. Configure model monitoring within the AWS Sagemaker to perform continuous validation of the model.