Deployment

Batch Inference

Definition

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.

Related Terms

Real-Time Inference

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.

Inference

The process of running a trained model on new data to produce predictions or generated outputs. Inference cost and latency are the dominant operational concerns in production AI, particularly for large generative models that can cost cents per request at scale.

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.

Knowing the Terms Is Step One. Applying Them Is Step Two.

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