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Database Design and Modeling with Google Cloud

You're reading from   Database Design and Modeling with Google Cloud Learn database design and development to take your data to applications, analytics, and AI

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781804611456
Length 234 pages
Edition 1st Edition
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Author (1):
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Abirami Sukumaran Abirami Sukumaran
Author Profile Icon Abirami Sukumaran
Abirami Sukumaran
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Table of Contents (18) Chapters Close

Preface 1. Part 1:Database Model: Business and Technical Design Considerations
2. Chapter 1: Data, Databases, and Design FREE CHAPTER 3. Chapter 2: Handling Data on the Cloud 4. Part 2:Structured Data
5. Chapter 3: Database Modeling for Structured Data 6. Chapter 4: Setting Up a Fully Managed RDBMS 7. Chapter 5: Designing an Analytical Data Warehouse 8. Part 3:Semi-Structured, Unstructured Data, and NoSQL Design
9. Chapter 6: Designing for Semi-Structured Data 10. Chapter 7: Unstructured Data Management 11. Part 4:DevOps and Databases
12. Chapter 8: DevOps and Databases 13. Part 5:Data to AI
14. Chapter 9: Data to AI – Modeling Your Databases for Analytics and ML 15. Chapter 10: Looking Ahead – Designing for LLM Applications 16. Index 17. Other Books You May Enjoy

Capturing the evolution of LLMs

LLMs are not mere algorithms; they are innovations, fueled by decades of research and breakthroughs. From their humble origins to today’s awe-inspiring models, LLMs have surpassed expectations, with unparalleled computational power and vast datasets. They are gateways to a new era of language processing, enabling machines to comprehend, generate, and manipulate text like never before. LLMs have evolved but they started as early as the 1950s.

The following diagram takes us through a high-level journey regarding the evolution of LLMs:

Figure 10.1: Evolution of LLMs

Figure 10.1: Evolution of LLMs

Some key research contributions have shaped the evolution of LLMs, such as the backpropagation algorithm (for training neural networks), the transformer architecture (for deriving context and meaning in sequential data), and the development of really large datasets. The evolution of LLMs is an ongoing process. As LLMs become more powerful, they will be...

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