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Inference is the process of applying a trained machine learning model to new data to produce predictions or decisions. It covers aspects such as latency, scalability, resource usage and model optimization for production deployments. Common use cases include real-time predictions, batch inference and on-device models.
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Inference is the execution of a trained machine-learning model on new input data to derive a prediction, classification, or other output.
The approach grew from statistical inference and became a distinct machine-learning operating phase as trained neural networks emerged. TensorFlow Serving exemplifies the move toward exposing trained models as running services for applications.
Think of a trained model as an experienced decision-maker with a fixed rulebook: new data is submitted, the model processes it, and returns a result. It does not learn again during this step.
An already fitted model processes new inputs.
The output may be a class, value, or decision.
A service makes the model available to applications.
Separating training from inference supports operating, scaling, and monitoring ML features. It also makes latency, model version, and input quality explicit operational concerns.
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