AI Engineering Building Applications With Foundation Models 1st Edition

AI Engineering: Building Applications with Foundation Models

AI Engineering: Building Applications with Foundation Models is a practical guide to designing and deploying real-world AI applications using modern foundation models. It explains how AI engineering differs from traditional machine learning by focusing on scalable, production-ready systems. The book covers the full lifecycle of AI development, including model selection, data handling, evaluation, and deployment. It also highlights challenges such as reliability, performance, and risk management. Aimed at developers and practitioners, it provides frameworks and strategies to transform AI from experimental prototypes into robust, efficient, and impactful applications across various domains.

What Stands Out

• Foundation Models: Leverage powerful foundation models to build sophisticated AI applications, ensuring a strong foundation for innovations across various domains and enhancing productivity in development processes.
• Practical Insights: Gain hands-on experience through practical examples and case studies, offering clear guidance to both beginners and experienced developers aiming to implement AI solutions effectively.
• Comprehensive Content: Explore an extensive range of topics covering AI engineering, providing readers with a thorough understanding of both theoretical concepts and real-world applications, making it essential for aspiring AI engineers.

Book / Product Details

Recent breakthroughs in AI have not only increased demand for AI products but also lowered the barriers to entry for those who want to build AI products. The model-as-a-service approach has transformed AI from an esoteric discipline into a powerful development tool that anyone can use. Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications. In this book, author Chip Huyen discusses AI engineering: the process of building applications with readily available foundation models.

The book starts with an overview of AI engineering, explaining how it differs from traditional ML engineering and discussing the new AI stack. The more AI is used, the more opportunities there are for catastrophic failures, and therefore, the more important evaluation becomes. This book discusses different approaches to evaluating open-ended models, including the rapidly growing AI-as-a-judge approach.

AI application developers will discover how to navigate the AI landscape, including models, datasets, evaluation benchmarks, and the seemingly infinite number of use cases and application patterns. You'll learn a framework for developing an AI application, starting with simple techniques and progressing toward more sophisticated methods, and discover how to efficiently deploy these applications. Understand what AI engineering is and how it differs from traditional machine learning engineering Learn the process for developing an AI application, the challenges at each step, and approaches to address them Explore various model adaptation techniques, including prompt engineering, RAG, fine-tuning, agents, and dataset engineering, and understand how and why they work Examine the bottlenecks for latency and cost when serving foundation models and learn how to overcome them Choose the right model, dataset, evaluation benchmarks, and metrics for your needs

Chip Huyen works to accelerate data analytics on GPUs at Voltron Data. Previously, she was with Snorkel AI and NVIDIA, founded an AI infrastructure startup, and taught Machine Learning Systems Design at Stanford. She's the author of the book Designing Machine Learning Systems, an Amazon bestseller in AI.

AI Engineering builds on and complements Designing Machine Learning Systems (O'Reilly).

Who Should Buy this Book?

• AI Practitioners: Professionals seeking to enhance their skills in applying foundation models for developing intelligent applications and solutions.
• Students: Students in computer science or related fields looking to gain insights into modern AI engineering techniques and practices.
• Developers: Software developers eager to integrate AI functionalities into applications and leverage foundation models for innovative projects.

Who Should Consider First Before Buying this Book?

• Beginners: Individuals new to AI or programming may find the content too advanced without prior foundational knowledge.
• Non-technical Users: Users without a technical background may struggle to understand the complex concepts presented in this book.
• Casual Readers: Those looking for light reading or basic introductions to AI may find the material too in-depth and technical.