Usage Considerations
The API_Request in-database function is used for model scoring and inference calculations.
Before using the API_Request in-database function, the AWS endpoint of Amazon SageMaker or analytic model should have been trained and deployed on AWS.
You also need the endpoint address, region and AWS credentials (which have permissions to use this in-database function) to execute a query to score Vantage data with this AWS analytic service.
Usage Example: Use SageMaker Endpoint for Scoring Vantage Data
In this example, an Amazon SageMaker model has been deployed to the AWS US-East-2 region with the endpoint 'sagemaker-xgboost-2021-10-20-15-43-44-623'. The authentication fields are intentionally left as ‘replace with your AWS credentials’. The expected input fields to this AWS analytic model are in the Vantage table ‘NEW_FINANCIAL_TRANS’.
SELECT rec_id, output as fraud_risk_score
FROM tapidb.API_Request
(
ON NEW_FINANCIAL_TRANS
USING AUTHORIZATION('{"Access_ID":"replace with your AWS Access ID",
"Session_Token":"replace with your AWS Session Token",
"Region":"us-east-2"}')
ENDPOINT('sagemaker-xgboost-2021-10-20-15-43-44-623')
CONTENT_TYPE('csv')
KEY_START_INDEX('1')
) as DT
;
API_Request query result:
rec_id fraud_risk_score
597417 0.8734374642372131
392307 0.5901673436164856
52268 0.9538228511810303
76809 0.47017282247543335
744410 0.9500066637992859
751493 0.5542758703231812
676641 0.881127119064331
146036 0.9368776082992554
631585 0.42199018597602844
45837 0.4777362048625946
51034 0.2817979156970978
484301 0.9431955814361572
603917 0.9265019297599792
554445 0.7330561280250549
12892 0.6860849857330322