Traditional machine learning systems rely on gradient-based optimization, batch training, and high computational cost. The Function Model, renamed from Morph Model, represents a fundamentally different paradigm, where learning is expressed as localized functional updates, applied instantly and deterministically. This enables continuous adaptation, streaming training, drift-free behavior, and human-like incremental refinement. This paper provides a conceptual overview of the architecture, using simple examples to illustrate patch behavior, inference structure, and stability properties. Implementation details are omitted and available under NDA for organizations evaluating deployment.