How do you decide between batch prediction and real-time model serving for production workloads?

Example Concept: Batch prediction involves processing large datasets at once, often during off-peak hours, to generate predictions that can be stored and retrieved later. This approach is efficient for non-time-critical applications, such as generating daily reports or updating recommendation systems. Real-time model serving, on the other hand, involves deploying models that can provide predictions instantly as new data arrives. This is crucial for applications like fraud detection, where immediate responses are necessary. The choice between these methods should consider the trade-offs between latency, computational cost, and infrastructure complexity.