Neural inference is the process of using a trained neural network to produce a prediction, classification, or other output from new input data. During inference, the model applies patterns and parameters learned during training to information it has not previously processed. Unlike training, which focuses on adjusting the model, inference focuses on efficiently generating results from the completed model.
For example, an AI-powered camera can analyze an incoming image and determine whether it contains a particular object. The trained neural network processes the image and produces its prediction without updating the model itself. Neural inference is used in applications such as image recognition, speech processing, natural language systems, recommendation engines, robotics, autonomous devices, and real-time edge AI.
