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Item #:
228958251
AI Engineering: Building Applications with Foundation Models
Item #:
228958251
XOF 47216
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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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What Stands Out
Foundation Model Integration
Leverage advanced foundation models to streamline application development, ensuring high-performance and scalability while reducing time-to-market for innovative solutions.
Hands-On Practicality
Engaging, hands-on approach allows users to build real-world applications, enhancing learning outcomes and equipping engineers with applicable skills for immediate impact.
Comprehensive Guidance
In-depth coverage of AI engineering principles and practices provides clarity and direction, helping professionals navigate the complexities of AI-driven application development.
Product Details
| 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?
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Data Scientists
Ideal for data scientists looking to integrate advanced AI features into their applications using foundation models.
-
Software Developers
Great for software developers who want to enhance their programming skills with artificial intelligence capabilities.
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AI Enthusiasts
Perfect for AI enthusiasts eager to learn about building applications that leverage the latest foundation models in AI.
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Beginners Without Background
Not suitable for absolute beginners in programming or AI who lack foundational knowledge.
Product Description
AI Engineering: Building Applications with Foundation Models
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Machine Theory Editorial Review
**** The book *AI Engineering: Building Applications with Foundation Models* by Chip Huyen has garnered an overwhelmingly positive reception from customers, primarily for its clarity, depth, and accessibility. Reviewers highlight the expertly structured content, which begins with high-level AI concepts and gradually delves into more technical topics, catering to a broad audience, including both beginners and experienced developers. Readers appreciate that the book simplifies many complex aspects of AI, making them approachable and understandable. Customers who initially found the book's length daunting have expressed satisfaction, noting that it provides an equivalent educational experience to a graduate-level course. The author's background as an AI expert and educator at Stanford lends credence to the material, which is often praised for its thorough yet straightforward explanations free from excessive jargon. The audio version of the book has also received accolades for its accessibility, allowing readers to engage with the content during commutes, further enhancing its appeal. Notably, the book is seen as a valuable investment, illuminating the path for readers looking to deepen their understanding of AI and transition into AI engineering roles. Overall, *AI Engineering* is highly recommended as a foundational resource for both newcomers and those seeking to elevate their expertise in the evolving field of AI. **
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Pros
- Clear and insightful explanations of complex AI concepts.
- Suitable for beginners and experienced developers alike.
- Well-structured starting with high-level concepts before moving to technical details.
- Accessible format, with positive feedback on the audiobook.
- Encourages deeper learning and provides a strong foundation for AI engineering.
- A valuable resource for those transitioning careers to AI.
Cons
- Some sections may not address every reader's specific needs.
Product Price History
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XOF 47216
Order now and get it around Friday, October 23
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Features & Benefits
- Discover the transformative model-as-a-service approach in AI.
- Learn how AI engineering differs from traditional ML engineering.
- Navigate the AI landscape with various models, datasets, and evaluation benchmarks.
- Progress from simple techniques to sophisticated methods for AI application development.
- Explore model adaptation techniques like prompt engineering and fine-tuning.
- Understand latency and cost bottlenecks in serving foundation models.
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