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Item #:
102249565
AI Engineering: Building Applications with Foundation Models
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Item #:
102249565
TTD 483
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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.
Product Details
- Recent breakthroughs in AI have not only increased demand for AI products, they've 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 upon and is complementary to Designing Machine Learning Systems (O'Reilly).
| Publisher | O'Reilly Media |
| Publication date | January 7, 2025 |
| Edition | 1st |
| Language | English |
| Print length | 532 pages |
| ISBN-10 | 1098166302 |
| ISBN-13 | 978-1098166304 |
| Item Weight | 2.05 pounds (930 grams) |
| Dimensions | 6.9 x 1.1 x 9 inches (17.5 x 2.8 x 22.9 cm) |
Who Should Buy?
-
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.
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Developers
Software developers eager to integrate AI functionalities into applications and leverage foundation models for innovative projects.
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Beginners
Individuals new to AI or programming may find the content too advanced without prior foundational knowledge.
Product Description
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Customer Questions & Answers
-
Question:
What is 'AI Engineering: Building Applications with Foundation Models' about?
Answer: This book serves as a comprehensive guide to understanding and building applications using foundation models in AI. It covers the principles of AI engineering, methodologies for leveraging foundation models, and practical examples of applying these technologies in real-world scenarios. Readers gain insights into the architectural design, integration techniques, and deployment strategies that can help them succeed in developing AI-driven solutions. -
Question:
Who is the target audience for this book?
Answer: The primary audience for this book includes software engineers, data scientists, AI researchers, and technology students interested in deep learning and AI applications. Additionally, professionals in various industries looking to implement AI solutions will find this resource invaluable. By providing practical insights and industry best practices, the book empowers readers to harness the potential of foundation models effectively. -
Question:
What are foundation models and why are they important?
Answer: Foundation models are large-scale machine learning models pre-trained on diverse datasets, capable of performing a variety of tasks with limited fine-tuning. Their importance lies in their versatility and efficiency, enabling developers to build sophisticated AI applications without starting from scratch. By understanding foundation models, readers can leverage state-of-the-art technology in areas such as natural language processing, computer vision, and more, drastically reducing time-to-market for new AI solutions. -
Question:
What programming languages are discussed in this book?
Answer: While the book primarily focuses on the concepts of AI engineering, it extensively covers programming in Python, which is widely used in AI and machine learning. Additionally, readers may encounter references to other languages such as R for data analysis and JavaScript for web development. The integration of diverse programming languages provides a well-rounded understanding of how to implement foundation models across different platforms. -
Question:
Are there any practical examples included in the book?
Answer: Yes, the book is rich in practical examples and case studies that illustrate the application of foundation models across various industries. Every chapter includes hands-on projects that guide readers through real-world scenarios, such as deploying chatbots, image recognition systems, and more. These examples help bridge the gap between theory and practice, equipping readers with the skills needed to apply AI engineering principles effectively. -
Question:
What skills will I learn by reading this book?
Answer: Readers will gain a solid understanding of foundational AI concepts, including model training, evaluation, and deployment. In addition, they will learn how to effectively use popular libraries like TensorFlow and PyTorch for implementing AI solutions. The book prepares readers to tackle complex challenges in AI engineering, enhancing their skill set for careers in technology and data science. -
Question:
Is this book suitable for beginners in AI?
Answer: Yes, the book is structured to accommodate readers at various skill levels, including beginners. It starts with fundamental concepts and gradually introduces more advanced topics, ensuring that all readers can follow along. The clear explanations, combined with practical examples, make it accessible for those new to AI engineering while still offering valuable insights for experienced practitioners. -
Question:
How can I apply the knowledge from this book in my career?
Answer: By applying the techniques and insights gained from this book, readers can enhance their ability to design and develop AI applications in their respective fields. Whether you're aiming for a role in AI research, software development, or data analysis, the skills taught in this book are highly relevant and transferable. Readers can showcase their knowledge by creating innovative projects and contributing to AI initiatives in their organizations. -
Question:
What resources accompany this book?
Answer: The book may include supplementary resources such as online repositories, code examples, and access to a community of readers. These resources are designed to enhance the learning experience by providing additional materials that reinforce key concepts. Engaging with these resources allows readers to apply what they've learned and collaborate with others in the field, further enriching their understanding of AI engineering. -
Question:
Where can I buy 'AI Engineering: Building Applications with Foundation Models 1st Edition' in Trinidad and Tobago?
Answer: You can buy 'AI Engineering: Building Applications with Foundation Models 1st Edition' on Ubuy, a reliable online marketplace that offers a wide selection of books and educational materials. Ubuy provides a user-friendly shopping experience and ensures that you can easily find this title along with related resources to enhance your understanding of AI engineering.
Machine Theory Editorial Review
AI Engineering: Building Applications with Foundation Models is a practical guide focusing on real-world AI engineering challenges, providing clear explanations and actionable examples to help developers and ML engineers build production-ready systems. With 532 pages of content, the book starts with high-level concepts, making it accessible for those new to AI. Readers have noted its effectiveness, especially for beginners, allowing them to connect various concepts easily. Many have praised the clear communication style, which aids in understanding complex topics, making it reminiscent of a graduate class, thus enhancing readers' fluency in AI engineering principles.
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Pros
- Clear explanations make complex concepts accessible
- Practical examples for building AI applications
- Well-suited for both beginners and experienced developers
- Engaging writing style ensures fluent reading
- In-depth content prepares readers for advanced topics
Cons
- Length may be daunting for some readers
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Features & Benefits
- Learn the essentials of AI engineering and its distinction from traditional ML engineering.
- Discover a framework for developing AI applications from simple to advanced techniques.
- Explore model adaptation techniques like prompt engineering and fine-tuning.
- Understand the bottlenecks related to latency and cost in serving foundation models.
- Choose the right models, datasets, and evaluation metrics tailored to your needs.
- Written by industry expert Chip Huyen, offering insights into practical AI application development.
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