Part IV — The Scalability Layer
This part addresses one of the central challenges of modern AI systems: scaling training efficiently across multiple GPUs and nodes. Through hands-on case studies using TensorFlow and PyTorch, it explores different forms of parallelism, distributed execution strategies, and performance trade-offs.
Part IV represents a key convergence point in the book, bringing together infrastructure knowledge, execution models, and AI frameworks. It can be read as a core component of courses focused on scalable deep learning, even when earlier parts are only partially covered.