Data Science Solutions on Azure
The Rise of Generative AI and Applied AI
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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 S...
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 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 Learn
Get 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 For
Software engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.
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 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 Learn
Get 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 For
Software engineers and architects looking to deploy end-to-end Generative AI solutions on Azure with the latest tools and techniques.
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