A manufacturing company wants to monitor its devices for anomalous behavior.

The task involves periodic inference (hourly batches of telemetry data) that can be processed in bulk, with the total processing time under 5 minutes. The most cost-effective solution for this batch prediction scenario is SageMaker Batch Transform, as it avoids the need for always-on endpoints.

“Use Batch Transform when you: … Don’t need a persistent endpoint and want to process batches of data at once. This is typically the most cost-efficient way to process large volumes of input data periodically.”

Since the use case doesn't require low latency or asynchronous real-time response, and processing takes only minutes, Batch Transform is the optimal fit.