Chip Huyen is a renowned data scientist, artificial intelligence (AI), and machine learning (ML) expert, with a wealth of experience in both academic and professional settings. Currently serving as the Vice President of AI and Open Source Software at Voltron Data, Chip focuses on GPU-native data processing and promoting open data standards, such as Ibis, Apache Arrow, and Substrait. She is also recognised for her entrepreneurial spirit, having co-founded Claypot AI, a real-time ML platform that was later acquired. Her contributions to the field are notable not only for their technical depth but also for their practical application in industries, particularly through her work at companies like Netflix, NVIDIA, and Snorkel AI.
A graduate of Stanford University, Chip’s academic achievements are equally impressive. She has taught several courses on machine learning systems, including the highly regarded CS 329S: Machine Learning Systems Design. Her lectures laid the groundwork for her book, “Designing Machine Learning Systems”, which has since become an Amazon #1 bestseller in AI. This book, hailed for its practical insights into building effective ML systems, has cemented Chip’s position as an authoritative voice in the rapidly evolving field of machine learning.
Chip’s professional journey extends across some of the most influential tech companies in the world. She has held key roles at NVIDIA, Snorkel AI, Netflix, and Primer, where she developed cutting-edge machine learning tools and solutions. Notably, she contributed to launching Coc Coc, Vietnam’s second-most popular web browser, which boasts over 20 million monthly active users. Her influence has also been acknowledged by LinkedIn, which included her among the Top Voices in Software Development in 2019 and the Top Voices in Data Science & AI in 2020.
As a keynote speaker, Chip is well-versed in delivering insights on a wide range of technological topics, from AI infrastructure to real-time machine learning. Her unique background, which spans both industry and academia, enables her to speak authoritatively on the challenges and opportunities in data science and machine learning. Drawing from her rich experience, she provides audiences with valuable perspectives on how to design and implement scalable AI systems in the real world, making her a sought-after speaker in the technology community.
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