batch inference

The process of inferring predictions on multiple unlabeled examples divided into smaller subsets ("batches"). Batch inference can take advantage of the parallelization features of accelerator chips. That is, multiple accelerators can simultaneously infer predictions on different batches of unlabeled examples, dramatically increasing the number of inferences per second. See Production ML systems: Static versus dynamic inference in Machine Learning Crash Course for more information.

Practitioners refer to batch inference when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.