A batch is a set of examples processed together in one training or inference operation. In model training, batches determine how many examples contribute to a parameter update during an iteration. Batch-based processing can improve computational efficiency and control memory use.
The set of examples used in one training iteration. The batch size determines the number of examples in a batch. See epoch for an explanation of how a batch relates to an epoch. See Linear regression: Hyperparameters in Machine Learning Crash Course for more information.
The set of examples used in one training iteration. The batch size determines the number of examples in a batch.
Amazon Machine Learning: An operation that processes multiple input data observations at one time (asynchronously). Unlike real-time predictions, batch predictions aren't available until all predictions have been processed.