Question

A retail company retrains a churn-prediction model weekly. Every night, they need to generate churn scores for their entire customer base of 40 million records stored as Parquet files in S3. The scoring job must complete before business hours, but there is no requirement for individual real-time predictions during the day. The ML engineer wants the most cost-effective deployment approach that avoids paying for idle compute. Which SageMaker deployment option should the engineer choose?

Accepted Answer

Use a SageMaker Batch Transform job scheduled nightly to process the S3 dataset

Answer

Deploy a real-time endpoint with autoscaling and invoke it in a loop each night

Answer

Deploy a Serverless Inference endpoint and send each record as an individual invocation

Answer

Configure an Asynchronous Inference endpoint and submit the full dataset as one payload