Priyanshi Singh – författare

Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads. The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You''ll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You''ll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you''ll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning. Data Science Solutions on Azure will reveal how the different Azure services work together using real life scenarios and how-to-build solutions in a single comprehensive cloud ecosystem. What You''ll LearnUnderstand big data analytics with Spark in Azure Databricks Integrate with Azure services like Azure Machine Learning and Azure SynapsDeploy, publish and monitor your data science workloads with MLOps Review data abstraction, model management and versioning with GitHubWho This Book Is ForData Scientists looking to deploy end-to-end solutions on Azure with latest tools and techniques. 

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.

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.