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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
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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

Summary

This chapter shed light on the intricate dynamics of language models in the realm of AI and also laid a robust foundation for understanding the complex world of LLMOps.

First, we looked into the historical progression of NLP, reviewing its evolution from rule-based systems to the advent of transformative LLMs. This journey highlighted the significant milestones and the technological advancements that have led to the development of sophisticated models such as GPT and Llama 2.

Next, we underscored the distinct challenges intrinsic to LLMOps, contrasting them with traditional MLOps. The scale, complexity, and unique requirements of LLMs require a specialized approach, differing significantly from conventional machine learning models.

After, we observed how LLMs are increasingly being integrated across various industries, reshaping the landscape of digital interaction and content generation. This integration signifies the growing influence and versatility of LLMs in practical...

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