AI Engineering
Foundation models have transformed the way organizations build AI products. Once limited by complex model training and scarce expertise, teams now face an overwhelming landscape of models, data, evaluation, and deployment challenges. Engineering leaders struggle to balance accuracy, latency, cost, and safety, while developers navigate fast-changing tools and unpredictable model behavior.
AI Engineering solves that. Using a structured, end-to-end framework, it unites product, engineering, and data teams to build applications that are reliable, scalable, and aligned with real business needs. It helps organizations find the right balance between capability, performance, and cost while adapting AI systems to real-world use cases.
The book provides a practical roadmap for adopting and maturing AI engineering practices, built from the author’s experience working with leading researchers, industry teams, and large-scale AI deployments.
Chip Huyen
AI engineer, educator, and author known for her work at Snorkel AI, NVIDIA, Stanford University, and her bestselling book Designing Machine Learning Systems. She specializes in AI infrastructure and has led and advised real-world AI deployment projects across industries.
