Déploiement

Real-Time Inference

Définition

Serving model predictions with low latency in response to individual live requests, typically within milliseconds to seconds. Real-time inference is required for customer-facing applications like chatbots, fraud detection, and autonomous control systems.

Termes Connexes

Batch Inference

Running a model on a large dataset in a single scheduled job rather than in real time. Batch inference is more cost-efficient than real-time serving for use cases such as nightly report generation, bulk document classification, or periodic customer scoring.

Model Serving

The process of deploying trained ML models to production environments where they can receive inputs and return predictions at scale. Model serving infrastructure must address throughput, latency, versioning, and cost while meeting SLAs.

Inference Cost

The compute and financial cost of running a model to produce a single prediction or generated response. Inference cost is often the dominant AI operational expenditure at scale and is managed through model compression, caching, quantization, and batching strategies.

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