Acknowledgments

A textbook of this scope is built on the contributions of many communities, researchers, and engineers.

To the Research Community

The ideas in this book trace their lineage through decades of research. We are indebted to the teams behind the foundational papers that shaped modern AI: Vaswani et al. for the transformer architecture, Radford et al. for demonstrating the power of generative pre-training, Devlin et al. for BERT's bidirectional representations, and the many researchers at universities and industry labs whose work fills these pages.

To the Open-Source Community

Modern AI engineering is inseparable from open-source software. This book relies heavily on:

  • PyTorch — The deep learning framework whose eager execution model and Pythonic design philosophy make it the ideal teaching tool
  • HuggingFace — For democratizing access to transformer models through the Transformers, Tokenizers, Datasets, and PEFT libraries
  • NumPy and SciPy — The bedrock of scientific computing in Python
  • scikit-learn — For making classical machine learning accessible and well-documented
  • Matplotlib and Seaborn — For bringing data to life through visualization
  • Weights & Biases — For experiment tracking and reproducibility tools
  • The Jupyter community — For creating the interactive computing environment that powers modern data science education

To the Educators

This textbook draws inspiration from exceptional educators and their works. We acknowledge the influence of:

  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville for Deep Learning
  • Christopher Bishop for Pattern Recognition and Machine Learning
  • Aurélien Géron for Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  • Sebastian Raschka for Machine Learning with PyTorch and Scikit-Learn
  • Jay Alammar for his transformative visual explanations of neural networks
  • Andrej Karpathy for making deep learning intuitive through code and lectures

To the Practitioners

The systems engineering chapters in this book reflect hard-won knowledge from engineers building AI systems at scale. We thank the teams at organizations who have openly shared their approaches to RAG, agent architectures, model serving, and MLOps through blog posts, conference talks, and open-source tools.

To Our Readers

Finally, thank you for investing your time in this book. AI engineering is a field where understanding the fundamentals pays compound interest. We hope this textbook serves as both your guide and your reference for years to come.