Top AI & Engineering Books
Build a Large Language ModelBook
by Sebastian Raschka PhD
A hands-on guide to building large language models from scratch.
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AI EngineeringBook
by Chip Huyen
A modern practical guide to AI engineering, deployment, and production systems, by Chip Huyen.
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Machine Learning System Design: A Guide to Interviewing and Exceeding ExpectationsBook
by Ali Aminian
A practical and interview-focused guide by Ali Aminian on designing machine learning systems, with a focus on clarity, communication, and real-world problem-solving.
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Designing Machine Learning Systems Book
by Chip Huyen
Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications, by Chip Huyen.
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Scaling Laws for Neural Language ModelsPaper
by Jared Kaplan et al.
A critical paper studying how scaling affects language model performance.
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Language Models are Few-Shot LearnersPaper
by Tom B. Brown et al. (OpenAI GPT-3)
The original GPT-3 paper showing the power of large-scale language models.
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BERT: Pre-training of Deep Bidirectional TransformersPaper
by Jacob Devlin et al.
The paper that introduced BERT, a landmark in NLP and pretraining.
View PaperAI and the Future of WorkBook
by Dirk Slama
How AI is reshaping engineering, organizations, and work.
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Building Machine Learning Systems with PythonBook
by Luís Pedro Coelho, Willi Richert
A practical guide to building ML systems with Python.
View BookThe Art of Feature EngineeringBook
by Pablo Bruce, Bruce Bruce, Peter Gedeck
An essential guide to feature engineering for real-world ML systems.
View BookMachine Learning Design PatternsBook
by Valliappa Lakshmanan, Sara Robinson, Michael Munn
Design patterns and best practices for building production ML systems.
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Effective Data Science InfrastructureBook
by Villi Tuulos
A modern guide to designing data science infrastructure and engineering ML pipelines.
View BookReliable Machine LearningBook
by Chip Huyen
Principles and practices for building reliable machine learning systems.
View BookSoftware Engineering at GoogleBook
by Titus Winters, Tom Manshreck, Hyrum Wright
Lessons and insights from Google’s experience building scalable software.
View BookBuilding Machine Learning Powered ApplicationsBook
by Emmanuel Ameisen
A hands-on guide to taking ML projects from prototype to production.
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Machine Learning EngineeringBook
by Andriy Burkov
A practical guide to building production ML systems.
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