Deep Learning, Ian Goodfellow, Yoshua Bengio, and Aaron Courville, 2016 (MIT Press) - This comprehensive textbook provides the mathematical and conceptual foundations of deep learning, including detailed explanations of neural network architectures, training algorithms (like backpropagation), and the distinction between model training and inference.
MLOps Engineering at Scale, Carl Osipov, 2022 (O'Reilly Media) - Offers practical guidance on designing and managing machine learning systems in production, covering architectural considerations for building infrastructure optimized for both the training and inference phases of AI workloads.
CS230: Deep Learning, Andrew Ng, Kian Katanforoosh, 2024 (Stanford University) - This well-regarded Stanford University course provides comprehensive materials on deep learning, including lectures and resources that distinguish between the computational demands and hardware requirements for AI model training and inference.