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Data science solutions on Azure = the rise of generative AI and applied AI /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science solutions on Azure/ by Julian Soh, Priyanshi Singh.
其他題名:	the rise of generative AI and applied AI /
作者:	Soh, Julian.
其他作者:	Singh, Priyanshi.
出版者:	Berkeley, CA :Apress : : 2024.,
面頁冊數:	xiii, 289 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Introduction and Update of AI in the Modern Enterprise -- Chapter 2: Generative AI and Large Language Models -- Chapter 3: Deploy and Explore Azure OpenAI -- Chapter 4: Designing a Generative AI Solution -- Chapter 5: Implementing a Generative AI Solution -- Chapter 6: Prompt Engineering Techniques, Small Language Models, and Fine Tuning -- Chapter 7: Semantic Kernel -- Chapter 8: Structured Data, Codex, Agents, and DBCopilot -- Chapter 9: Azure AI Services.
Contained By:	Springer Nature eBook
標題:	Microsoft Azure (Computing platform) -
電子資源:	https://doi.org/10.1007/979-8-8688-0914-9
ISBN:	9798868809149

Data science solutions on Azure = the rise of generative AI and applied AI /
Soh, Julian.

Data science solutions on Azurethe rise of generative AI and applied AI /[electronic resource] :by Julian Soh, Priyanshi Singh. - Second edition. - Berkeley, CA :Apress :2024. - xiii, 289 p. :ill., digital ;24 cm.

Chapter 1: Introduction and Update of AI in the Modern Enterprise -- Chapter 2: Generative AI and Large Language Models -- Chapter 3: Deploy and Explore Azure OpenAI -- Chapter 4: Designing a Generative AI Solution -- Chapter 5: Implementing a Generative AI Solution -- Chapter 6: Prompt Engineering Techniques, Small Language Models, and Fine Tuning -- Chapter 7: Semantic Kernel -- Chapter 8: Structured Data, Codex, Agents, and DBCopilot -- Chapter 9: Azure AI Services.

This revamped and updated book focuses on the latest in AI technology-Generative AI. It builds on the first edition by moving away from traditional data science into the area of applied AI using the latest breakthroughs in Generative AI. Based on real-world projects, this edition takes a deep look into new concepts and approaches such as Prompt Engineering, testing and grounding of Large Language Models, fine tuning, and implementing new solution architectures such as Retrieval Augmented Generation (RAG) You will learn about new embedded AI technologies in Search, such as Semantic and Vector Search. Written with a view on how to implement Generative AI in software, this book contains examples and sample code. In addition to traditional Data Science experimentation in Azure Machine Learning (AML) that was covered in the first edition, the authors cover new tools such as Azure AI Studio, specifically for testing and experimentation with Generative AI models. What's New in this Book Provides new concepts, tools, and technologies such as Large and Small Language Models, Semantic Kernel, and Automatic Function Calling Takes a deeper dive into using Azure AI Studio for RAG and Prompt Engineering design Includes new and updated case studies for Azure OpenAI Teaches about Copilots, plugins, and agents What You'll Learn Get up to date on the important technical aspects of Large Language Models, based on Azure OpenAI as the reference platform Know about the different types of models: GPT3.5 Turbo, GPT4, GPT4o, Codex, DALL-E, and Small Language Models such as Phi-3 Develop new skills such as Prompt Engineering and fine tuning of Large/Small Language Models Understand and implement new architectures such as RAG and Automatic Function Calling Understand approaches for implementing Generative AI using LangChain and Semantic Kernel See how real-world projects help you identify great candidates for Applied AI projects, including Large/Small Language Models.

ISBN: 9798868809149

Standard No.: 10.1007/979-8-8688-0914-9doiSubjects--Topical Terms:

3201298
Microsoft Azure (Computing platform)

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Data science solutions on Azure = the rise of generative AI and applied AI /
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