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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

Generative AI application development frameworks

In this section, we will focus on the popular generative AI-based app development frameworks used by developers today in their applications, as they add functionality and extensibility to LLMs.

But why do we need to modernize existing ML applications to use intelligent generative AI in the first place? Let’s compare and contrast application characteristics without generative AI and the modernized applications infused with generative AI.

Current ML applications have some of these common characteristic limitations:

  • They are constrained with their interactions, especially with generative AI services.
  • They are hard-coded and usually have a fixed dataset. For example, one can leverage certain datasets to train certain ML models, and those models are fixed.
  • If they then want to change a model within an application or if they want to change the dataset entirely, they will need to again retrain the model, which is...
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