How AI Models Learn

Machine learning systems learn patterns from data through an optimization process.

During training, a model produces outputs from examples, an objective measures how those outputs differ from the desired behavior, and an optimization process adjusts the model parameters.

The important concept is not that the model memorizes a simple collection of answers. Instead, training changes a large set of parameters so that the model becomes better at representing useful relationships present in its training data.

Inference is a separate stage. Once training has produced a model, inference uses the learned parameters to generate outputs for new inputs.

Training and Inference