Overview

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   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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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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