How does inference pipeline batching strategy influence bias and variance in real-time ML systems?

Batching improves throughput and stability but can introduce bias due to delayed processing and reduced responsiveness.

Inference batching groups multiple requests together to improve GPU utilization and throughput. While this reduces computational variance and stabilizes system performance, it introduces latency, which can lead to stale predictions in fast-changing environments.

Bias arises when delayed predictions no longer reflect current user state. Variance decreases because batching smooths out per-request noise. However, overly large batch sizes can degrade responsiveness and reduce personalization accuracy.…