Название: Data Science Solutions on Azure: The Rise of Generative AI and Applied AI, 2nd Edition
Автор: Julian Soh, Priyanshi Singh
Издательство: Apress
Год: 2024
Страниц: 294
Язык: английский
Формат: pdf
Размер: 18.9 MB

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