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Generative AI for Cloud Solutions

You're reading from   Generative AI for Cloud Solutions Architect modern AI LLMs in secure, scalable, and ethical cloud environments

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781835084786
Length 300 pages
Edition 1st Edition
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Authors (2):
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Paul Singh Paul Singh
Author Profile Icon Paul Singh
Paul Singh
Anurag Karuparti Anurag Karuparti
Author Profile Icon Anurag Karuparti
Anurag Karuparti
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Integrating Cloud Power with Language Breakthroughs
2. Chapter 1: Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities FREE CHAPTER 3. Chapter 2: NLP Evolution and Transformers: Exploring NLPs and LLMs 4. Part 2: Techniques for Tailoring LLMs
5. Chapter 3: Fine-Tuning – Building Domain-Specific LLM Applications 6. Chapter 4: RAGs to Riches: Elevating AI with External Data 7. Chapter 5: Effective Prompt Engineering Techniques: Unlocking Wisdom Through AI 8. Part 3: Developing, Operationalizing, and Scaling Generative AI Applications
9. Chapter 6: Developing and Operationalizing LLM-based Apps: Exploring Dev Frameworks and LLMOps 10. Chapter 7: Deploying ChatGPT in the Cloud: Architecture Design and Scaling Strategies 11. Part 4: Building Safe and Secure AI – Security and Ethical Considerations
12. Chapter 8: Security and Privacy Considerations for Gen AI – Building Safe and Secure LLMs 13. Chapter 9: Responsible Development of AI Solutions: Building with Integrity and Care 14. Part 5: Generative AI – What’s Next?
15. Chapter 10: The Future of Generative AI – Trends and Emerging Use Cases 16. Index 17. Other Books You May Enjoy

Putting it all together

Before we arrive at the last major section of this chapter to look at an actual case study and best practices, we felt it is helpful to put all the generative AI categories together and understand how data flows from one into another and vice-versa.

Earlier, we shared the CI/CD pipeline flow using Prompt Flow within the LLMOps construct. Now, we will take a macro look, beyond just the LLM, at how the LLM application development stack messages would flow across the generative AI ecosystem, using categories to organize the products and services.

While we do not endorse any specific services or technology, except our employer, our goal here is to show how a typical LLM flow would appear using various generative AI toolsets/products/services. We have organized each of the workloads by category, represented in the light gray boxes, along with a few of the products or services, as examples within each category. Then, we use arrows to show how typical traffic...

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