AI Engineering: Building Applications with Foundation Models
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Everyone, including those with minimal or no prior AI experience, can now leverage AI models to build applications.
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Produkta informācija
- 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). Daha fazla göster
| Date of publication | January 7, 2025 |
| Prints | 1. |
| Language | English |
| Print Length | 528 pages |
| ISBN-10 | 1098166302 |
| ISBN-13 | 978-1098166304 |
| Dimensions | 17.53 x 2.79 x 22.86 cm |
Who Should Buy?
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AI Enthusiasts
Individuals eager to understand foundational models and their applications in various AI projects and scenarios.
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Developers
Software engineers looking to integrate foundation models into their applications or seeking to enhance their AI skills.
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Researchers
Academics and professionals conducting research on AI advancement, focusing on foundational model development and innovations.
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Beginners
Those with minimal knowledge of AI might struggle to grasp the complex concepts introduced in this paper.
PRODUKTA APRAKSTS
Klientu jautājumi un atbildes
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jautājums:
Who is this book intended for?
atbildi: This book is for anyone interested in AI application development, regardless of their prior experience. -
jautājums:
What will I learn from this book?
atbildi: You'll learn about AI engineering, model adaptation techniques, and practical steps to build and deploy AI applications. -
jautājums:
Is prior knowledge of machine learning required?
atbildi: No, the book is designed for readers with minimal or no prior AI experience.
İngilizce Baskı Format: Kağıt Kapak Editorial Review
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- Comprehensive and insightful content
- Easy to follow for beginners
- Practical examples and applications
- Up-to-date with latest trends
Mīnusi
- Some sections could be more detailed.
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Funkcijas un priekšrocības
- 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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