Network architecture in the context of artificial intelligence and machine learning pertains to the design and arrangement of a neural network's layers, nodes, and connections. This architecture dictates how data flows through the network and how the network processes information. Common types of network architectures include feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs), each tailored to specific tasks. The choice of architecture is crucial, as it impacts the model's ability to learn and generalize from data. Advances in network architecture, such as the introduction of the Transformer model for natural language processing, have revolutionized how AI systems are built, leading to significant improvements in performance across various applications.