Generative AI in Action
Generative AI in Action
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分類：英文書＞自然科普＞電腦資訊＞系統設計追蹤? 追蹤分類後，您會在第一時間收到分類新品通知。
- 作者： Amit,Bahree 追蹤 ? 追蹤作者後，您會在第一時間收到作者新書通知。
- 出版社： Manning Publications 追蹤 ? 追蹤出版社後，您會在第一時間收到出版社新書通知。
- 出版日：2024/10/02
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內容簡介
Generative AI can transform your business by streamlining the process of creating text, images, and code. This book will show you how to get in on the action! Generative AI in Action is the comprehensive and concrete guide to generative AI you've been searching for. It introduces both AI's fundamental principles and its practical applications in an enterprise context--from generating text and images for product catalogs and marketing campaigns, to technical reporting, and even writing software. Inside, author Amit Bahree shares his experience leading Generative AI projects at Microsoft for nearly a decade, starting well before the current GPT revolution. Inside Generative AI in Action you will find: - A practical overview of of generative AI applications
- Architectural patterns, integration guidance, and best practices for generative AI
- The latest techniques like RAG, prompt engineering, and multi-modality
- The challenges and risks of generative AI like hallucinations and jailbreaks
- How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You'll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you'll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You'll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside - Best practices for deploying Generative AI apps
- Production-quality RAG
- Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1
1 Introduction to generative AI
2 Introduction to large language models
3 Working through an API: Generating text
4 From pixels to pictures: Generating images
5 What else can AI generate?
Part 2
6 Guide to prompt engineering
7 Retrieval-augmented generation: The secret weapon
8 Chatting with your data
9 Tailoring models with model adaptation and fine-tuning
Part 3
10 Application architecture for generative AI apps
11 Scaling up: Best practices for production deployment
12 Evaluations and benchmarks
13 Guide to ethical GenAI: Principles, practices, and pitfalls
A The book's GitHub repository
B Responsible AI tools
- Architectural patterns, integration guidance, and best practices for generative AI
- The latest techniques like RAG, prompt engineering, and multi-modality
- The challenges and risks of generative AI like hallucinations and jailbreaks
- How to integrate generative AI into your business and IT strategy Generative AI in Action is full of real-world use cases for generative AI, showing you where and how to start integrating this powerful technology into your products and workflows. You'll benefit from tried-and-tested implementation advice, as well as application architectures to deploy GenAI in production at enterprise scale. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology In controlled environments, deep learning systems routinely surpass humans in reading comprehension, image recognition, and language understanding. Large Language Models (LLMs) can deliver similar results in text and image generation and predictive reasoning. Outside the lab, though, generative AI can both impress and fail spectacularly. So how do you get the results you want? Keep reading! About the book Generative AI in Action presents concrete examples, insights, and techniques for using LLMs and other modern AI technologies successfully and safely. In it, you'll find practical approaches for incorporating AI into marketing, software development, business report generation, data storytelling, and other typically-human tasks. You'll explore the emerging patterns for GenAI apps, master best practices for prompt engineering, and learn how to address hallucination, high operating costs, the rapid pace of change and other common problems. What's inside - Best practices for deploying Generative AI apps
- Production-quality RAG
- Adapting GenAI models to your specific domain About the reader For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI. About the author Amit Bahree is Principal Group Product Manager for the Azure AI engineering team at Microsoft. The technical editor on this book was Wee Hyong Tok. Table of Contents Part 1
1 Introduction to generative AI
2 Introduction to large language models
3 Working through an API: Generating text
4 From pixels to pictures: Generating images
5 What else can AI generate?
Part 2
6 Guide to prompt engineering
7 Retrieval-augmented generation: The secret weapon
8 Chatting with your data
9 Tailoring models with model adaptation and fine-tuning
Part 3
10 Application architecture for generative AI apps
11 Scaling up: Best practices for production deployment
12 Evaluations and benchmarks
13 Guide to ethical GenAI: Principles, practices, and pitfalls
A The book's GitHub repository
B Responsible AI tools
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