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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 recommender solution using Azure OpenAI

Now that we have set up all the essential services in our Azure portal, we can begin constructing our solution. To develop the code, we will be working within an Azure Machine Learning Studio (Azure ML Studio) notebook. For this solution, we will be utilizing Python version 3.12. As mentioned earlier, any version above 3.7.1 should work seamlessly. You can access the code we’re using on this GitHub profile; I recommend referring to it as you progress through this chapter. Within the repository, you’ll find a requirements.txt file that lists all the Python libraries necessary for our solution. Please review this file and use the following command to install the required packages: !pip install requirements.txt. Then, proceed as follows:

  1. Create a Kaggle account.

    Navigate to https://www.kaggle.com/datasets/shivamb/netflix-shows?resource=download, then either download the CSV file or go to Settings and generate an API...

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