What is the purpose of using batch inference in Azure?

Batch inference is a technique used in machine learning where predictions are generated for a large group of data points at once rather than processing them individually in real-time. This approach is particularly beneficial when the goal is to handle substantial datasets efficiently, as it allows for the simultaneous execution of numerous predictions, which can save time and computational resources.

Batch inference is most useful in scenarios where immediate response times are not critical, such as processing historical data, generating periodic reports, or running large-scale analyses on datasets to extract insights. By processing data in batches, the overall workflow can be optimized, making it more efficient when compared to real-time inference that may be resource-intensive and slower when dealing with large volumes of data.

In contrast, real-time feedback on model predictions, data compression, and monitoring model performance pertain to different needs and strategies that don't align with the primary purpose of batch inference, which is about handling large datasets effectively.