AI Engineering by Chip Huyen is a practical guide to building AI-powered applications using foundation models. As artificial intelligence continues to evolve, this book provides a structured approach to integrating pre-trained AI models into real-world products. Huyen explores key concepts such as model selection, data processing, evaluation metrics, deployment strategies, and ethical considerations, making it an essential resource for engineers, developers, and product managers working in the AI space.

With a focus on hands-on implementation, AI Engineering helps readers understand how to navigate the AI landscape without the need for extensive data or deep expertise in training models. It covers practical use cases, common challenges, and best practices for creating scalable and efficient AI-driven applications.

Why Read This Book

• Provides a clear framework for integrating foundation models into AI applications.
• Covers real-world use cases, from chatbots to recommendation systems.
• Explains evaluation methods to ensure AI models perform effectively.
• Discusses ethical considerations in AI development and deployment.
• Written by an AI expert with experience in machine learning engineering and product development.

About the Author

Chip Huyen is an AI engineer, writer, and educator specializing in machine learning systems. She has worked at leading AI companies and research institutions, focusing on MLOps, AI infrastructure, and scalable ML applications. Huyen has also taught machine learning at Stanford University and is known for her contributions to the field of AI deployment. In addition to AI Engineering, she has authored Designing Machine Learning Systems, a widely respected book on MLOps and scalable AI systems.