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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

Summary

In this chapter, we covered the basis of generative AI intersecting with software development. We covered three popular programming generative AI application frameworks: Semantic Kernel, LangChain, and LlamaIndex. We also introduced LLMOps, a comprehensive framework for managing the lifecycle of a generative AI ecosystem and how Prompt Flow can simplify the management of an LLMOps strategy; together, all of these components form a comprehensive framework for developing and deploying generative AI applications and services.

We also described the lifecycle of an LLM model itself to round out the lifecycle discussion.

As we look at extensibility and automating, we delved into the world of agents and autonomous agents, such as AutoGen and AutoGPT, which can work autonomously to address extremely complex problems by using a few techniques such as chaining or networking LLMs together in collaboration.

Finally, we looked at an actual case study of a large organization and...

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