True or False: Batch prediction is optimized for handling multiple prediction requests simultaneously.

Batch prediction is indeed optimized for handling multiple prediction requests simultaneously. This approach allows for processing large datasets in one go, which is more efficient than handling individual prediction requests one at a time. Batch prediction takes advantage of parallel processing capabilities and can significantly reduce the time and resources required to generate predictions across many data points.

This method is particularly beneficial when working with models deployed in environments where many inputs need predictions, such as in recommendation systems, financial forecasting, or any scenario where a bulk of data needs to be processed for insights. It contrasts with online prediction, where individual requests are processed in real-time, making batch prediction a preferred choice for scenarios where latency is less critical, but throughput is a priority.

In this context, the other options do not accurately represent the capabilities of batch prediction. Batch prediction can handle large volumes effectively, not just small batches or specific model types, making the assertion that it is optimized for multiple simultaneous requests correct.