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Essential Guide to LLMOps

You're reading from   Essential Guide to LLMOps Implementing effective strategies for Large Language Models in deployment and continuous improvement

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835887509
Length 190 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Ryan Doan Ryan Doan
Author Profile Icon Ryan Doan
Ryan Doan
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Table of Contents (14) Chapters Close

Preface 1. Part 1: Foundations of LLMOps FREE CHAPTER
2. Chapter 1: Introduction to LLMs and LLMOps 3. Chapter 2: Reviewing LLMOps Components 4. Part 2: Tools and Strategies in LLMOps
5. Chapter 3: Processing Data in LLMOps Tools 6. Chapter 4: Developing Models via LLMOps 7. Chapter 5: LLMOps Review and Compliance 8. Part 3: Advanced LLMOps Applications and Future Outlook
9. Chapter 6: LLMOps Strategies for Inference, Serving, and Scalability 10. Chapter 7: LLMOps Monitoring and Continuous Improvement 11. Chapter 8: The Future of LLMOps and Emerging Technologies 12. Index 13. Other Books You May Enjoy

The evolution of NLP and LLMs

NLP’s inception can be traced back to the 1950s and 1960s, a period characterized by exploratory efforts and foundational research. During these early years, NLP was primarily driven by rule-based methods and statistical approaches, setting the stage for more complex developments in the decades to follow.

Rule-based NLP relied heavily on sets of handcrafted rules. These rules were designed by linguists and computer scientists to instruct computers on how to interpret and process language. For instance, early systems would break down text into components such as nouns, verbs, and adjectives, and then apply a series of predefined rules to analyze sentence structures and meanings. This approach was limited by its reliance on explicit rules, making the systems brittle and unable to understand the nuances of human language.

Around the same time, statistical methods introduced a new paradigm in NLP. Unlike rule-based systems, statistical NLP did...

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