Numéro d'article:
224587920
AI Engineering: Building Applications with Foundation Models
90% des répondants recommanderaient ceci à un ami
Numéro d'article:
224587920
TND 319
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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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Ce qui se démarque
Foundation Model Integration
Seamlessly integrates advanced foundation models, enabling developers to build robust applications that leverage state-of-the-art AI capabilities for enhanced performance and accuracy.
Comprehensive Learning Resources
Offers extensive educational materials and tutorials, empowering users of all skill levels to master AI engineering and effectively utilize foundation models in their projects.
Versatile Application Development
Facilitates the creation of diverse applications across industries, ensuring versatility and adaptability by addressing specific user needs with tailored AI solutions.
Détails du produit
- 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 | 7 Jan. 2025 |
| Language | English |
| Print length | 532 pages |
| ISBN-10 | 1098166302 |
| ISBN-13 | 978-1098166304 |
| Dimensions | 17.53 x 2.79 x 22.86 cm |
À qui est-ce destiné ?
-
Developers and Engineers
Perfect for professionals looking to leverage foundation models to enhance application development and streamline workflows.
-
AI Researchers
Ideal for researchers exploring advanced AI methodologies and seeking to incorporate foundation models into their projects.
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Tech Startups
Beneficial for startups aiming to integrate cutting-edge AI technologies into their products without extensive resources.
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Beginner Users
Not suitable for absolute beginners who lack background knowledge in AI and programming concepts.
DESCRIPTION DU PRODUIT
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Questions et réponses des clients
-
question:
Who is this book intended for?
répondre: This book is for anyone interested in AI application development, regardless of their prior experience. -
question:
What will I learn from this book?
répondre: You'll learn about AI engineering, model adaptation techniques, and practical steps to build and deploy AI applications. -
question:
Is prior knowledge of machine learning required?
répondre: No, the book is designed for readers with minimal or no prior AI experience.
English edition Chip Huyen Format: Paperback Editorial Review
AI Engineering: Building Applications With Foundation Models is a must-have resource for developers and technology leaders focused on AI-powered applications. The book offers a well-structured exploration of foundation models, covering everything from model selection to deployment trade-offs, making it invaluable for both newcomers and seasoned professionals. Readers appreciate the accessibility of complex information, with clear examples that make it easy to grasp. It also successfully highlights no-code approaches, catering to a diverse audience. Although some sections may be challenging for non-technical readers, the book provides a comprehensive overview that is both insightful and engaging for anyone interested in AI engineering.
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Avantages
- Accessible to both beginners and experienced readers
- Clear examples following theoretical explanations
- Focus on no-code approaches for practical application
- Well-structured content covering key AI topics
- Valuable resource for technology leaders
Les inconvénients
- Some sections may feel challenging for non-technical readers
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TND 319
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Caractéristiques et avantages
- 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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