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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

Creating a Multimodal Multi-Agent Framework with the Azure OpenAI Assistants API

In this chapter, we will explore how to build a system using the Azure OpenAI Assistants API that allows multiple intelligent agents to work together. These agents use advanced language models to understand and generate natural language, making them capable of performing tasks independently and making decisions on their own.

For example, imagine a team of AI agents collaborating to create and enhance images based on user input. One agent could generate an initial image, another agent could refine the details, and a third agent could adjust the colors and textures. This collaborative process results in high-quality and intricate outputs that a single agent might struggle to achieve alone. This example serves as a comprehensive guide for developers looking to harness the capabilities of multiple AI agents working in concert. It shows how agents can communicate and collaborate to process complex tasks...

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