What is the difference between parametric and non-parametric models?

Parametric models have a fixed number of parameters regardless of training data size. After training, the data is no longer needed — the model is summarized by its parameters. Examples: linear regression, logistic regression, neural networks. They are faster at inference and require less memory but make stronger assumptions. Non-parametric models grow in complexity with the data — the "parameters" are effectively the training data itself. Examples: KNN, Kernel SVM, Gaussian Processes. They are more flexible and make fewer assumptions but require storing all training data and are slower at inference.