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Generative AI for Cloud Solutions
Generative AI for Cloud Solutions

Generative AI for Cloud Solutions: Architect modern AI LLMs in secure, scalable, and ethical cloud environments

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Profile Icon Paul Singh Profile Icon Anurag Karuparti
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.9 (9 Ratings)
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Generative AI for Cloud Solutions

Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities

During the last few decades, we have seen unprecedented progress in the world of artificial intelligence (AI) and machine learning (ML) due to the rise of computing, especially cloud computing, and the massive influx of data from the digital revolution. In 2022, the subset of AI known as generative AI emerged as a significant turning point. We have surpassed an inflection point in AI and we believe this will boost incredible productivity and growth in society in the coming years. This is the field of conversational AI powered by large language models (LLMs), a fascinating paradigm where computers learn and generate human-like text, images, audio, and video, engaging with us in increasingly interactive and intelligent ways. The transformative potential of LLMs, epitomized by models, such as OpenAI’s GPT-based ChatGPT, marks a major shift in how we interact with technology. Generative AI models now have improved...

Evolution of conversation AI

Understanding the evolution of conversational AI is crucial for learning generative AI as it provides foundational knowledge and context. This historical perspective reveals how AI technologies have progressed from simple, rule-based systems to complex machine learning and deep learning models that are core to both conversational and generative AI.

This section explores the evolution of conversational AI, culminating in an in-depth look at LLMs, the technological backbone of contemporary chatbots.

What is conversational AI?

Conversational AI refers to technologies that enable machines to engage in human-like dialogue, comprehend complex commands, and respond intelligently. This is achieved through machine learning and natural language processing capabilities, enabling the system to learn, understand, and improve over time. The following figure demonstrates one such conversation:

Figure 1.1 – Conversations with Alexa

Figure 1.1 – Conversations with...

Introduction to generative AI

Generative AI refers to a field of AI (as stated in the preceding figure) that focuses on creating or generating new content, such as images, text, music, video, code, 3D objects, or synthetic data that is not directly copied or replicated from existing data. It involves training deep learning models to understand patterns and relationships within a given dataset and then using that knowledge to generate novel and unique content. The following is a visualization of what generative AI is:

Figure 1.3 – What is generative AI?

Figure 1.3 – What is generative AI?

It is a broad field whose primary function is to generate novel content. Examples of generative AI models include image generation models such as DALL-E and MidJourney, text generation models such as GPT-4, PaLM, and Claude, code generation models such as Codex, audio generation tools such as MusicLM, and video generation models such as SORA.

The rise of generative AI in 2022-23

Generative AI...

Trending models, tasks, and business applications

Generative AI has a wide range of applications across various industries, presenting several use cases that can bring significant benefits to businesses, and the applications are continuing to grow at a fast pace. In this section, we will discuss popular tasks and models and examine the latest emerging business applications that have gained significant traction recently.

Let’s begin with text generation models.

Text

Text generation models can be used for diverse tasks as outlined here. In the following, we have mentioned the most popular tasks that we have seen architecting solutions with our customers:

  • Summarization: They can condense long documents, such as textbook chapters or detailed product descriptions, into concise summaries while retaining the key information.
  • Question answering: These models can provide accurate answers to questions, which is particularly useful in automating the creation of FAQ...

Cloud computing for scalability, cost optimization, and security

Cloud computing has been instrumental in bringing LLMs to a wider audience. LLMs use large-scale GPU processing to learn and generate human-like text, image, audio, and video, engaging in increasingly interactive and intelligent ways.

This section highlights several advantages of leveraging LLMs in a cloud environment:

  • Scalability: Cloud computing enables users to access high-performance computing such as GPUs as necessary to run LLMs. This makes it easy to scale applications as required based on consumption needs.

    Since LLM models such as GPT are heavy API-driven workloads, there is a need for API management services, such as Azure APIM, that help achieve scalability, security, and high availability across regions. They can also capture telemetry that can help determine token usage and error logging across organizations. We discuss scaling strategies on Azure in Chapter 7.

  • Affordability: There is no need...

From vision to value – navigating the journey to production

Developing an idea and moving it into production is a multi-phase process that typically involves ideation, validation, development, testing, and deployment. The multi-phase process of developing an idea and moving it into production is crucial because it methodically transforms a concept into a viable product.

Take a look at the following image about overlooking a crucial aspect:

Figure 1.8 – Two entrepreneurs engaging in a humorous discussion about overlooking expenses

Figure 1.8 – Two entrepreneurs engaging in a humorous discussion about overlooking expenses

The above image satirically showcases how some organizations claim to build AI from scratch, when in reality, they're just utilizing API calls to services like OpenAI. It humorously uncovers this exaggeration when asked about the Open AI bills, mocking the notion of starting from scratch.

Each phase serves a distinct purpose: ideation fosters innovation, validation ensures market demand and feasibility...

Summary

The aim of this introductory chapter was to highlight the history, core concepts, and other essential information necessary for readers to develop an end-to-end generative AI solution on the cloud. We have explored the evolution of chatbots from simple rule-based systems to multimodal, context-aware, and action-oriented agentic LLMs. We delved into the rise of generative AI, focusing on LLMs and foundation models as well as their relationship and key attributes. The differences between open source and closed source models were examined, alongside trending business applications drawn from our experiences. In the rapidly evolving landscape of AI, we’ve examined a few leading models, including text, image, audio, and video generation. These models represent the forefront of AI technology, showcasing remarkable capabilities in creating high-quality, lifelike content. We then highlighted how cloud computing facilitates the development of secure, scalable, cost-efficient,...

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Key benefits

  • Gain foundational GenAI knowledge and understand how to scale GenAI/ChatGPT in the cloud
  • Understand advanced techniques for customizing LLMs for organizations via fine-tuning, prompt engineering, and responsible AI
  • Peek into the future to explore emerging trends like multimodal AI and autonomous agents
  • Purchase of the print or Kindle book includes a free PDF eBook

Description

Generative artificial intelligence technologies and services, including ChatGPT, are transforming our work, life, and communication landscapes. To thrive in this new era, harnessing the full potential of these technologies is crucial. Generative AI for Cloud Solutions is a comprehensive guide to understanding and using Generative AI within cloud platforms. This book covers the basics of cloud computing and Generative AI/ChatGPT, addressing scaling strategies and security concerns. With its help, you’ll be able to apply responsible AI practices and other methods such as fine-tuning, RAG, autonomous agents, LLMOps, and Assistants APIs. As you progress, you’ll learn how to design and implement secure and scalable ChatGPT solutions on the cloud, while also gaining insights into the foundations of building conversational AI, such as chatbots. This process will help you customize your AI applications to suit your specific requirements. By the end of this book, you’ll have gained a solid understanding of the capabilities of Generative AI and cloud computing, empowering you to develop efficient and ethical AI solutions for a variety of applications and services.

Who is this book for?

This artificial intelligence book is for aspiring cloud architects, data analysts, cloud developers, data scientists, AI researchers, technical business leaders, and technology evangelists looking to understanding the interplay between GenAI and cloud computing. Some chapters provide a broad overview of GenAI, which are suitable for readers with basic to no prior AI experience, aspiring to harness AI's potential. Other chapters delve into technical concepts that require intermediate data and AI skills. A basic understanding of a cloud ecosystem is required to get the most out of this book.

What you will learn

  • Get started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function together
  • Understand how we started applying NLP to concepts like transformers
  • Grasp the process of fine-tuning and developing apps based on RAG
  • Explore effective prompt engineering strategies
  • Acquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIs
  • Discover how to scale and secure GenAI systems, while understanding the principles of responsible AI
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Table of Contents

17 Chapters
Part 1:Integrating Cloud Power with Language Breakthroughs Chevron down icon Chevron up icon
Chapter 1: Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities Chevron down icon Chevron up icon
Chapter 2: NLP Evolution and Transformers: Exploring NLPs and LLMs Chevron down icon Chevron up icon
Part 2: Techniques for Tailoring LLMs Chevron down icon Chevron up icon
Chapter 3: Fine-Tuning – Building Domain-Specific LLM Applications Chevron down icon Chevron up icon
Chapter 4: RAGs to Riches: Elevating AI with External Data Chevron down icon Chevron up icon
Chapter 5: Effective Prompt Engineering Techniques: Unlocking Wisdom Through AI Chevron down icon Chevron up icon
Part 3: Developing, Operationalizing, and Scaling Generative AI Applications Chevron down icon Chevron up icon
Chapter 6: Developing and Operationalizing LLM-based Apps: Exploring Dev Frameworks and LLMOps Chevron down icon Chevron up icon
Chapter 7: Deploying ChatGPT in the Cloud: Architecture Design and Scaling Strategies Chevron down icon Chevron up icon
Part 4: Building Safe and Secure AI – Security and Ethical Considerations Chevron down icon Chevron up icon
Chapter 8: Security and Privacy Considerations for Gen AI – Building Safe and Secure LLMs Chevron down icon Chevron up icon
Chapter 9: Responsible Development of AI Solutions: Building with Integrity and Care Chevron down icon Chevron up icon
Part 5: Generative AI – What’s Next? Chevron down icon Chevron up icon
Chapter 10: The Future of Generative AI – Trends and Emerging Use Cases Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Subhasish Ghosh Aug 27, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Just finished perusing "𝑮𝒆𝒏𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝑨𝑰 𝒇𝒐𝒓 𝑪𝒍𝒐𝒖𝒅 𝑺𝒐𝒍𝒖𝒕𝒊𝒐𝒏𝒔: 𝑨𝒓𝒄𝒉𝒊𝒕𝒆𝒄𝒕 𝒎𝒐𝒅𝒆𝒓𝒏 𝑨𝑰 𝑳𝑳𝑴𝒔 𝒊𝒏 𝒔𝒆𝒄𝒖𝒓𝒆, 𝒔𝒄𝒂𝒍𝒂𝒃𝒍𝒆, 𝒂𝒏𝒅 𝒆𝒕𝒉𝒊𝒄𝒂𝒍 𝒄𝒍𝒐𝒖𝒅 𝒆𝒏𝒗𝒊𝒓𝒐𝒏𝒎𝒆𝒏𝒕𝒔", authored by my Microsoft colleagues, Paul S. & Anurag Karuparti, Packt Publications. An excellent read, this is a nicely detailed book for those looking to start your journey into Generative AI.𝐓𝐚𝐫𝐠𝐞𝐭 𝐚𝐮𝐝𝐢𝐞𝐧𝐜𝐞: Aspiring Cloud Architects, Data Analysts, Cloud Dev, Data Scientists, AI Researchers, Tech Business Leaders, and Tech Evangelists.✅ What this book is: Broad high-level overview of GenAI❎ What this book is not: This is not a technical deep dive into Algorithms, Transformer Model variants or Code for LLMs/LMMs/SLMs etc.5 things that makes this book a good read:1️⃣ Get started with the essentials of generative AI, LLMs, and ChatGPT, and understand how they function together.2️⃣ Understand how we started applying NLP to concepts like transformers. Grasp the process of fine-tuning and developing apps based on RAG.3️⃣ Explore effective prompt engineering strategies. Acquire insights into the app development frameworks and lifecycles of LLMs, including important aspects of LLMOps, autonomous agents, and Assistants APIs.4️⃣ Discover how to scale and secure GenAI systems, while understanding the principles of responsible AI.5️⃣ Loved the chapters #4 & #5, which focuses on Retrieval-Augmented Generation (RAG), Vector DB essentials, role it plays in RAG, chunking strategies, KNNs versus ANNs, Azure Prompt Flow, Essential Prompt Engineering techniques and more. These 2 chapters are excellent for those looking to get a solid grasp on both the theoretical and conceptual aspects & practical aspects of RAG & Prompt Engineering.Overall, highly recommended book.
Amazon Verified review Amazon
Steven Fernandes May 07, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
An indispensable guide for anyone venturing into the world of generative AI and large language models (LLMs). This book offers a comprehensive exploration, from the basic concepts of natural language processing (NLP) and transformers to the nuanced details of fine-tuning and application development using retrieval-augmented generation (RAG). It includes in-depth discussions on prompt engineering, application frameworks, and the lifecycle of LLMs, along with crucial considerations in LLMOps, autonomous agents, and Assistants APIs. Additionally, the book provides practical advice on scaling and securing GenAI systems and emphasizes the principles of responsible AI. A thorough and enlightening read for both novices and experienced practitioners in the field.
Amazon Verified review Amazon
homePC Jun 10, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I did not set a high expectation when I started reading, but it was extremely good. I like the approach and the concise way of explaining complicated topics in an easy-to-understand way.
Amazon Verified review Amazon
Om S Jun 14, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book is a brilliant introduction to the integration of cloud computing with generative AI. The chapter 'Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities' really expanded my understanding of the potential in combining these technologies. I found 'NLP Evolution and Transformers' very enlightening, particularly for its deep dive into how NLPs and LLMs are evolving. The 'Fine Tuning' chapter provided practical insights into tailoring AI applications to specific domains. 'RAGs to Riches' introduced an innovative approach to leveraging external data in AI models. The strategies shared in 'Effective Prompt Engineering Strategies' are essential for anyone looking to refine AI outputs. 'Developing and Operationalizing LLM-Based Cloud Applications' was a great guide on the lifecycle of AI apps. The security chapter addressed crucial considerations needed to protect AI systems. 'Responsible Development of AI Solutions' emphasized ethical practices which are vital in AI development. Lastly, 'Future of Generative AI' offered an exciting peek into what the next era of AI might hold.
Amazon Verified review Amazon
Jose Arias May 16, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very good and clear book that has helped me understand the fundamentals of generative ai. I love how it starts from scratch and builds up going through all the components.
Amazon Verified review Amazon
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