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Model serving describes the systems and infrastructure that expose trained machine learning models to production traffic, handling scaling, versioning, routing and observability. It includes serving APIs, model lifecycle management and resource orchestration. The goal is reliable, low‑latency inference and reproducible deployment pipelines.
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Theoretical construct: explains a term, principle, or mental model.
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Model serving exposes a trained machine-learning model as a callable service that accepts inputs and produces predictions or decisions.
Model serving grew from the need to move trained models reliably from development and training environments into running applications. Inference systems and projects such as TensorFlow Serving shaped the technical approach with versioning, scaling, and runtime operation.
Treat the model as a versioned service: a request enters, defined preprocessing runs, the model computes an output, and monitoring plus rollout rules keep operation controlled.
The service applies a trained model to new inputs.
A schema, version, and error behaviour define how clients call the model.
Scaling, latency, rollouts, and monitoring keep predictions reliable in operation.
Model serving connects machine learning with software operations. It matters when models must be used in applications with reproducible versions and scalable runtime behaviour; data and model drift remain operational risks.
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