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Data Science Solutions on Azure

The Rise of Generative AI and Applied AI
TaschenbuchKartoniert, Paperback
280 Seiten
Englisch
Springererscheint am14.11.20242. Aufl.
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 BookProvides new concepts, tools, and technologies such as Large and Small Language Models, Semantic Kernel, and Automatic Function CallingTakes a deeper dive into using Azure AI Studio for RAG and Prompt Engineering designIncludes new and updated case studies  for Azure OpenAITeaches about Copilots, plugins, and agents What You'll LearnGet up to date on the important technical aspects of Large Language Models, based on Azure OpenAI as the reference platformKnow about the different types of models: GPT3.5 Turbo, GPT4, GPT4o, Codex, DALL-E, and Small Language Models such as Phi-3Develop new skills such as Prompt Engineering and fine tuning of Large/Small Language ModelsUnderstand and implement new architectures such as RAG and Automatic Function CallingUnderstand approaches for implementing Generative AI using LangChain and Semantic KernelSee how real-world projects help you identify great candidates for Applied AI projects, including Large/Small Language Models Who This Book Is ForSoftware engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.mehr
Verfügbare Formate
TaschenbuchKartoniert, Paperback
EUR70,00
E-BookPDF1 - PDF WatermarkE-Book
EUR56,99

Produkt

KlappentextThis 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 BookProvides new concepts, tools, and technologies such as Large and Small Language Models, Semantic Kernel, and Automatic Function CallingTakes a deeper dive into using Azure AI Studio for RAG and Prompt Engineering designIncludes new and updated case studies  for Azure OpenAITeaches about Copilots, plugins, and agents What You'll LearnGet up to date on the important technical aspects of Large Language Models, based on Azure OpenAI as the reference platformKnow about the different types of models: GPT3.5 Turbo, GPT4, GPT4o, Codex, DALL-E, and Small Language Models such as Phi-3Develop new skills such as Prompt Engineering and fine tuning of Large/Small Language ModelsUnderstand and implement new architectures such as RAG and Automatic Function CallingUnderstand approaches for implementing Generative AI using LangChain and Semantic KernelSee how real-world projects help you identify great candidates for Applied AI projects, including Large/Small Language Models Who This Book Is ForSoftware engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.
Details
ISBN/GTIN979-8-8688-0913-2
ProduktartTaschenbuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2024
Erscheinungsdatum14.11.2024
Auflage2. Aufl.
Seiten280 Seiten
SpracheEnglisch
IllustrationenXV, 285 p. 10 illus.
Artikel-Nr.17370759

Inhalt/Kritik

Inhaltsverzeichnis
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.mehr

Autor


Julian Soh  is a software engineer at Microsoft, focusing on the areas of artificial intelligence and advanced analytics for Independent Software Vendors (ISVs) who develop software solutions based on the Microsoft technology stack. Prior to his current role, Julian worked extensively on major public cloud initiatives, such as SaaS (Microsoft 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations.

 

Priyanshi Singh is a software engineer and a data scientist by training. She is a data enthusiast by nature specializing in machine learning techniques applied to predictive analytics, computer vision, and natural language processing. She holds a master´s degree in Data Science from New York University and is currently a software solutions engineer at Microsoft helping the public sector to transform citizen services with Artificial Intelligence. She also leads a Meetup community based out of New York to help educate public sector employees via hands-on labs and discussions. Apart from her passion for learning new technologies and innovating with AI, she is a sports lover, a great badminton player, and enjoys playing billiards.
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