A manufacturing company runs a SageMaker scikit-learn model that labels each device as normal or anomalous based on 4 days of telemetry. Each device's 4-day telemetry is written as a separate file to S3 every hour. Running the model across all devices takes 5 minutes. What is the most cost-effective way to run the model over the telemetry for all devices?

SageMaker Batch Transform is the most cost-effective solution for this scenario because it is designed for offline, large-scale inference on an entire dataset. The company needs to process telemetry for all devices hourly, which aligns perfectly with Batch Transform's capabilities to process data in batches without needing a persistent endpoint. This avoids the continuous cost of a running endpoint. SageMaker Asynchronous Inference is suitable for large payload sizes or long-running inferences but still uses a persistent endpoint, incurring higher costs than Batch Transform for this use case. SageMaker Processing is designed for data pre-processing, post-processing, and model evaluation, not for running inference with a deployed model. A SageMaker multi-container endpoint is for real-time inference with complex deployment needs, which is overkill and more expensive for hourly batch processing.