Generative AI in Action

Amit Bahree

ISBN 10: 1633436942 / ISBN 13: 9781633436947

Verlag: Manning Publications Okt 2024, 2024

Beschreibung

Neuware - For enterprise architects, developers, and data scientists interested in upgrading their architectures with generative AI.

Bestandsnummer des Verkäufers 9781633436947

Bibliografische Details

• Titel: Generative AI in Action
• Verlag: Manning Publications Okt 2024
• Erscheinungsjahr: 2024
• Sprache: Englisch
• ISBN-10: 1633436942
• ISBN-13: 9781633436947
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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.

 

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!

Über die Autorin bzw. den Autor

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.