Chip Huyen
Also known as AI Engineering, Designing Machine Learning Systems
Chip Huyen writes about the engineering required to bring AI into production. Her earlier work includes ML tooling at NVIDIA, Snorkel AI, and Netflix, and she contributed to NeMo. She graduated from Stanford and taught a machine learning systems course there. Those lectures became the foundation for Designing Machine Learning Systems, published in 2022. Her April 2023 article on production LLM applications examined prompting, reliability, latency, and cost. AI Engineering followed in 2025 with an approach to applications built using foundation models. Her books and public repositories connect model capabilities with data, evaluation, deployment, and maintenance. Her public repositories add chapter summaries, prompt examples, case studies, and further reading that help developers connect the books with specific application problems.
- Works
- 9
- Citations
- 4
- h-index
- 1
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
-
ML tooling and company work
Working on machine learning tools
Huyen worked on machine learning tools at NVIDIA, Snorkel AI, and Netflix. At NVIDIA she contributed to NeMo, a framework for building and training models. She also founded and sold an AI infrastructure company. Her work brought her into the engineering problems behind production systems.
-
Stanford teaching
Turning system design into a course
After graduating from Stanford, Huyen taught machine learning systems there. The lectures became the foundation for Designing Machine Learning Systems. The teaching focus covered the practical decisions needed to move from a model experiment to a system used by other people.
-
2022
A book about the full system
Designing Machine Learning Systems organized production ML into an iterative process. It covered the connections among data, models, deployment, and changing requirements. The book supplied a common reference for engineers dealing with tasks that extend beyond the choice of a learning algorithm.
-
April 2023
Examining language model applications
Huyen published Building LLM Applications for Production in April 2023. She discussed prompt design, testing, cost, latency, and reliability. The article followed her work with companies and personal experiments, and made the difficulties of operating model-powered applications part of the public discussion.
-
2025
Publishing AI Engineering
AI Engineering addressed applications built with foundation models. Its companion repository contains chapter summaries, prompt examples, case studies, and further resources. The book extends her systems approach to models that developers can adapt through prompting, retrieval, and other application techniques.
-
Current public work
Maintaining practical learning resources
Her current site continues to link technical writing, books, public tools, and events. The AI Engineering repository preserves examples and reading material. Together, her resources support a teaching practice that helps developers reason about a complete application and check the behaviour of the system they build.
Sources and credits
Photo: Chip Huyen / huyenchip.com. License: Not stated by source. Source: huyenchip.com. Cropped to a square and resized. Ask us to remove or replace this photo.