Azure OpenAI Service for Cloud Native Applications: Designing, Planning, and Implementing Generative AI Solutions

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Get the details, examples, and best practices you need to build generative AI applications, services, and solutions using the power of Azure OpenAI Service. With this comprehensive guide, Microsoft AI specialist Adrián González Sánchez examines the integration and utilization of Azure OpenAI Service—using powerful generative AI models such as GPT-4 and GPT-4o—within the Microsoft Azure cloud computing platform.

To guide you through the technical details of using Azure OpenAI Service, this book shows you how to set up the necessary Azure resources, prepare end-to-end architectures, work with APIs, manage costs and usage, handle data privacy and security, and optimize performance. You’ll learn various use cases where Azure OpenAI Service models can be applied, and get valuable insights from some of the most relevant AI and cloud experts.

Ideal for software and cloud developers, product managers, architects, and engineers, as well as cloud-enabled data scientists, this book will help you:

Learn how to implement cloud native applications with Azure OpenAI ServiceDeploy, customize, and integrate Azure OpenAI Service with your applicationsCustomize large language models and orchestrate knowledge with company-owned dataUse advanced roadmaps to plan your generative AI projectEstimate cost and plan generative AI implementations for adopter companies

From the Publisher

From the Preface

I cannot hide my excitement. The 2022–2024 period has been one of the most amazing moments of the modern technology era. Some call it the “iPhone moment” of artificial intelligence, and a lot of people are now discovering the actual potential of AI. But I don’t think that’s all it is. I believe we are entering an exponential phase where all technological advancements move so fast that it is difficult to keep track of them. But that is wonderful. Several years of progress and industry competition in just a few months. What was often thought of as impossible (or even magic) is now a reality…and it’s just getting started.

That sense of innovation and complete disruption is how I felt the first time I tried the Azure OpenAI Studio. I got early access as an AI Specialist at Microsoft. It was a very early version, and definitively not the Studio and related features and models we have today, but it was very promising. Little did we know that this cloud-enabled service was about to become the superstar of the generative AI era. And it was a reality, not a concept or a future product. It was something we could use to create our very own GPT-style implementations, with different models and cost/performance trade-offs, but also with relatively low implementation and deployment complexity.

After a few months of testing and tracking new functionalities, OpenAI released ChatGPT. Boom. I have never seen such a viral moment related to AI technologies. Even at Microsoft, the feeling of being witnesses to something extraordinary was there, every day and evening, during countless “nerd” discussions with my colleagues. The key moment was the announcement of the “chat” functionality in Azure OpenAI Service, which allowed any company to test and deploy a ChatGPT-ish kind of instance for their own purposes for the first time. Then came Bing Chat (which would evolve into what we today call Microsoft Copilot). Boom ×2. That was the first time we saw the combination of classic search engines with a GPT chat experience, on the same screen…and it worked! People could get a direct answer to their precise needs instead of searching for information using keywords and having to determine the right answer themselves, and they were doing it with plain language. Not keywords, not complex combinations of words. Just asking for information and waiting for an answer.

Months passed and we started to deploy the first proof-of-concepts with Azure OpenAI. I’m part of a field team, so I was very close to the reality of the adopters—their understanding of what generative AI is, their envisioned use cases, their concerns, etc. I was also part of the AI community at Microsoft, which had plenty of energy and creativity to explore new approaches, discover new architectures, and learn about the most recent techniques and accelerators. Trust me, I wasn’t the only one feeling lucky in those moments. This was pure energy.

At one point, and I assume this was due to my academic background as a university professor, I felt like the vast amount of information—while very useful for any learner or adopter—was also a bit overwhelming for any company or individual trying to get started with generative AI and the Azure OpenAI Service. There was a lot of demand from companies around the world, and this technology deserved to be massively adopted, in a safe, responsible manner.

It was then that I started drafting the main concepts of a technical guide for application development with Azure OpenAI Service. Initially, it was just a way to keep track of all the URLs and pieces of information I was continuously collecting. Then, I continued adding my notes, based on my own implementation experiences. Finally, I kept changing or adding content based on the recurrent questions and discussions I was getting from clients, friends, and even family!

This was a great baseline, and I knew it could become an official technical guide, or even a full book. I decided to talk to my O’Reilly colleagues and present the topic. These conversations took only a matter of weeks. The potential was clear, but the challenge was huge: creating high-quality O’Reilly-level content, in a timely manner (as soon as possible) so all generative AI adopters could start reading and learning.

This has been one of my most challenging, but still rewarding, experiences. I feel really honored to write this book. So many Microsoft folks around the world could have done it, and for this reason, I took the opportunity very seriously. My main goal was to create something that would include all critical elements for Azure OpenAI learning, keeping in mind the (constantly) evolving context—showing the best features and implementation approaches, but knowing that there will be others soon and a continuously changing mix of generative availability and new features in preview. But that’s part of the charm, and the reason why I like this book and the creative process behind it so much.

One of my favorite things (and I hope you like it too) is the combination of the typical static content of a book, with the interactivity of online repositories, references to evolving documentation…and the incredible power of the guest interviews. Having such an amazing amount of talent and knowledge from a roster of AI pros is an authentic luxury, for you as readers and avid learners, but also for me as an AI professional.

Now, I hope that if you have decided to start reading this book, you are ready to explore each piece, from the core technical aspects to the other relevant business and ethical aspects that will help you during your first generative AI projects with Azure OpenAI Service.

Truly yours,

Adrián

ASIN ‏ : ‎ B0D8925ZSY
Publisher ‏ : ‎ O’Reilly Media; 1st edition (June 27, 2024)
Publication date ‏ : ‎ June 27, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 22159 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 437 pages

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