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Batch deployment runs inference over accumulated data on a schedule or as an asynchronous job. It is broadly used because it provides high throughput and cost efficiency when predictions do not need immediate, low-latency responses; real-time, streaming, and on-device serving are selected for more specialized latency or connectivity requirements.