What is ML system backpressure handling in streaming inference pipelines?

Backpressure handling prevents overload by controlling data flow in streaming ML systems.

Backpressure occurs when downstream ML inference services cannot process incoming data at the same rate it is produced. Without control, this leads to queue buildup, latency spikes, and system crashes. Techniques include load shedding, adaptive rate limiting, queue buffering, and feedback-based throttling. Streaming systems like Kafka or Flink implement backpressure signals to maintain stability.