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Solutions Architect's Handbook

You're reading from   Solutions Architect's Handbook Kick-start your career with architecture design principles, strategies, and generative AI techniques

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Product type Paperback
Published in Mar 2024
Publisher Packt
ISBN-13 9781835084236
Length 578 pages
Edition 3rd Edition
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Authors (2):
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Neelanjali Srivastav Neelanjali Srivastav
Author Profile Icon Neelanjali Srivastav
Neelanjali Srivastav
Saurabh Shrivastava Saurabh Shrivastava
Author Profile Icon Saurabh Shrivastava
Saurabh Shrivastava
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Toc

Table of Contents (20) Chapters Close

Preface 1. Solutions Architects in Organizations 2. Principles of Solution Architecture Design FREE CHAPTER 3. Cloud Migration and Cloud Architecture Design 4. Solution Architecture Design Patterns 5. Cloud-Native Architecture Design Patterns 6. Performance Considerations 7. Security Considerations 8. Architectural Reliability Considerations 9. Operational Excellence Considerations 10. Cost Considerations 11. DevOps and Solution Architecture Framework 12. Data Engineering for Solution Architecture 13. Machine Learning Architecture 14. Generative AI Architecture 15. Rearchitecting Legacy Systems 16. Solution Architecture Document 17. Learning Soft Skills to Become a Better Solutions Architect 18. Other Books You May Enjoy
19. Index

What is generative AI?

Generative AI is artificial intelligence with the remarkable ability to develop new content and ideas. This includes things like having conversations, creating stories, producing images and videos, and even making music.

In December 2022, the design team at the Laboratory for Artificial Intelligence in Design (AiDLab) located in Hong Kong orchestrated a groundbreaking fashion exhibition titled Fashion X AI (https://www.fashionxai.com/event-highlights-fashionshow). This showcase was unique because every design featured in the event was created by AI, drawing inspiration from mood boards, color palettes, and concepts provided by human designers.

Like other types of AI, generative AI relies on machine learning (ML) models. These models are quite large and are pre-trained using vast amounts of data. We often call these models foundation models (FMs).

The FMs we have today (like OpenAI GPT-4 or Google Gemini for large language tasks, Stable Diffusion...

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