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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 FREE CHAPTER
2. Chapter 1: Cloud Computing Meets Generative AI: Bridging Infinite Impossibilities 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

Case study – Global chat application deployment by a multinational organization

A global firm recently launched an advanced internal chat application featuring a Q&A support chatbot. This innovative tool, deployed across various Azure regions, integrates several large language models, including the specialized finance model, BloombergGPT. To meet specific organizational requirements, bespoke plugins were developed. It had an integration with Service Now, empowering the chatbot to streamline ticket generation and oversee incident actions.

In terms of data refinement, the company meticulously preprocessed its knowledge base (KB) information, eliminating duplicates, special symbols, and stop words. The KB consisted of answers to frequently asked questions and general information to various support-related questions. They employed fixed chunking approaches, exploring varied chunk sizes, before embedding these data into the Azure AI search. Their methodology utilized Azure...

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