Inference
Using a trained model to make predictions or generate output on new input.
Inference is the process of running new, unseen input through an already-trained model to produce a prediction, classification, or generated response. Unlike training — which adjusts model weights over large datasets and is compute-intensive — inference applies fixed weights, making it far faster per request but the primary source of recurring operational cost at scale. A key exam distinction is real-time inference (low-latency, synchronous, suited for interactive applications) versus batch inference (asynchronous processing of large datasets, lower cost per item). Amazon SageMaker supports both through endpoints and batch transform, while Amazon Bedrock exposes foundation-model inference via a managed API without provisioning infrastructure.
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