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  • Book Overview & Buying Azure OpenAI Essentials
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Azure OpenAI Essentials

Azure OpenAI Essentials

By : Amit Mukherjee, Adithya Saladi
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Azure OpenAI Essentials

Azure OpenAI Essentials

By: Amit Mukherjee, Adithya Saladi

Overview of this book

Find out what makes Azure OpenAI a robust platform for building AI-driven solutions that can transform how businesses operate. Written by seasoned experts from Microsoft, this book will guide you in understanding Azure OpenAI from fundamentals through to advanced concepts and best practices. The book begins with an introduction to large language models (LLMs) and the Azure OpenAI Service, detailing how to access, use, and optimize its models. You'll learn how to design and implement AI-driven solutions, such as question-answering systems, contact center analytics, and GPT-powered search applications. Additionally, the chapters walk you through advanced concepts, including embeddings, fine-tuning models, prompt engineering, and building custom AI applications using LangChain and Semantic Kernel. You'll explore real-world use cases such as QnA systems, document summarizers, and SQLGPT for database querying, as well as gain insights into securing and operationalizing these solutions in enterprises. By the end of this book, you'll be ready to design, develop, and deploy scalable AI solutions, ensuring business success through intelligent automation and data-driven insights.
Table of Contents (19 chapters)
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Part 1: Foundations of Generative AI and Azure OpenAI
5
Part 2: Practical Applications of Azure OpenAI: Real-World Use Cases
13
Part 3: Mastering Governance, Operations, and AI Optimization with Azure OpenAI

Azure vector databases

In the previous section, we explored various AOAI embedding models for generating vector embeddings. After creating these vectors, it’s essential to have a database optimized for storing and managing them effectively. The key distinction between a vector database and other types of databases is its capability to handle high-dimensional data. A vector database is specifically engineered to store data as high-dimensional vectors, which are mathematical representations of various features or attributes. Each vector comprises multiple dimensions, ranging from tens to thousands, depending on the data’s complexity and detail. These vectors are usually generated by applying transformation or embedding functions to raw data sources such as text, images, audio, video, and more. This type of database enables the indexing and querying of embeddings using vector search algorithms that assess vector distance or similarity. To ensure accurate retrieval of relevant...

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