predictiontime

Predictiontime, or inference latency, is the time from when input data becomes available to when a deployed model outputs its prediction. It is a key metric for the responsiveness of machine learning systems and is distinct from training time, which measures the model development process.

Predictiontime is usually reported as average per-instance latency and as tail metrics such as p95 or p99 to capture worst-case delays. It may refer to single-sample inference or batch processing, and definitions may include or exclude preprocessing and postprocessing steps.

Several factors influence predictiontime: model size and architecture; numerical precision; input size and data formatting; preprocessing and feature extraction; hardware (CPU, GPU, TPU, edge accelerators); software stack optimizations; and system load or queuing. In batching scenarios, throughput can improve while per-item latency may rise.

Low predictiontime is critical for real-time or interactive applications such as live recommendations, fraud detection, robotics, or autonomous systems. Optimization strategies include model compression (quantization, pruning), distillation, selecting efficient architectures, deploying on specialized hardware, optimizing the preprocessing pipeline, and using asynchronous or streaming inference. Trade-offs between accuracy, latency, and throughput must be balanced.