AI Engineering: 1st Edition
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AI Engineering: 1st Edition
The Essential Guide to Modern AI Engineering
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Detailed Overview:
AI Engineering, 1st Edition by Chip Huyen is a practical and forward-thinking guide designed to help engineers, developers, and technical teams build reliable, scalable, and production-ready artificial intelligence systems. Written by one of the leading voices in modern AI infrastructure and machine learning systems, this book bridges the gap between theoretical machine learning knowledge and real-world AI deployment.
The book focuses on the engineering challenges involved in developing AI-powered applications, including data pipelines, model training, evaluation, deployment, monitoring, optimization, and system scalability. Chip Huyen explains complex AI engineering concepts in a clear and accessible manner, helping readers understand how to design robust machine learning workflows for modern production environments.
Covering topics such as foundation models, MLOps, inference optimization, vector databases, retrieval-augmented generation (RAG), AI infrastructure, and responsible AI practices, the book reflects the latest advancements in applied artificial intelligence. Real-world examples, engineering insights, and practical implementation strategies make this resource valuable for both beginners and experienced practitioners.
Ideal for software engineers, machine learning engineers, data scientists, AI researchers, and technology professionals, AI Engineering serves as an essential reference for anyone building and deploying AI systems in today’s rapidly evolving technology landscape.
Title: AI Engineering: 1st Edition
Author: Chip Huyen
ISBN-13: 978-1098166304
ISBN-10: 1098166302
Publisher: O'Reilly Media
Binding: Paperback
No of Pages: 386
Language: English
Publisher Date: October 8, 2024
About Book:
AI Engineering, 1st Edition provides a comprehensive introduction to designing, building, deploying, and maintaining modern AI systems in production environments. The book explores practical machine learning engineering concepts including data processing, model deployment, system scalability, AI infrastructure, monitoring, and optimization.
Chip Huyen combines theoretical understanding with real-world engineering practices to help readers navigate challenges associated with production AI applications. The book covers emerging topics such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, MLOps workflows, and responsible AI development.
Designed for software engineers, AI practitioners, data scientists, and machine learning professionals, this resource emphasizes practical implementation strategies and scalable AI system design. Its modern approach and industry-focused content make it highly valuable for professionals building next-generation AI applications and infrastructure.
About Author:
Chip Huyen is a respected AI engineer, researcher, educator, and author specializing in machine learning systems, MLOps, and AI infrastructure. She is widely known for her expertise in production machine learning and has worked extensively in AI research, engineering, and education. Chip Huyen is also recognized for her influential contributions to the AI community through teaching, writing, and advising organizations on scalable AI system design. Her work focuses on bridging research innovation with practical AI engineering applications.
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