Data Science Solutions on Azure
Apress (Verlag)
979-8-8688-0913-2 (ISBN)
- Noch nicht erschienen - erscheint am 10.12.2024
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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, and implementing new solution architectures such as the Retrieval Augmented Generation (RAG) in machine learning. 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 have extended the use of AML to test and experiment with Large Language Models.
What's New in this Book
- Provides updated data sources and technologies, such as OneLake and Microsoft Fabric. Includes an in-depth look at Retrieval Augmented Generation (RAG) in search technology. Semantic and Vector Search have been added.
- Takes a deeper dive into using AML for RAG and Prompt Engineering
- Includes new and updated case studies for Azure OpenAI
- Teaches about Copilots and plugins
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
- Develop new skills such as Prompt Engineering and fine tuning of Large Language Models
- Understand and implement new architectures such as RAG
- 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 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.
Julian Soh is a software engineer and a cloud architect with Microsoft, focusing in 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 in major public cloud initiatives, such as SaaS (Microsoft 365), IaaS/PaaS (Microsoft Azure), and hybrid private-public cloud implementations. Priyanshi Singh is a senior artificial intelligence and machine learning technical specialist at Microsoft, specializing in designing end-to-end cloud solutions that leverage generative AI models and AI implementation best practices. She holds a master’s degree in data science from New York University and has a robust background as a data scientist, focusing on machine learning techniques for predictive analytics, computer vision, and natural language processing. Priyanshi is dedicated to helping the public sector and independent software vendors (ISVs) transform citizen services through artificial intelligence. She has been recognized as Microsoft's FY24 State and Local Government Pinnacle Winner for her exceptional contributions to AI adoption and the growth of Azure business. Additionally, Priyanshi is a sports enthusiast, excelling in badminton and enjoying golf and billiards.
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.
| Erscheinungsdatum | 22.11.2024 |
|---|---|
| Zusatzinfo | 258 Illustrations, black and white; XIII, 289 p. 258 illus. |
| Verlagsort | Berlin |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Netzwerke |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Azure • azure databricks • Big Data Analytics • ChatGPT • data abstraction • Data Scientist • DevOps • generative AI • gpt • Large Language Models • Prompt Flow |
| ISBN-13 | 979-8-8688-0913-2 / 9798868809132 |
| Zustand | Neuware |
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