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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
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Author (1):
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Ryan Doan Ryan Doan
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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

Introduction to LLMs and LLMOps

In this chapter, we’ll examine the historical evolution of natural language processing (NLP) and the milestones leading to large language models (LLMs), gaining both a historical and future-oriented perspective on large language model operations (LLMOps). LLMOps refers to the processes, tools, and best practices that are adapted for the operational management of LLMs in a production environment. Our journey will explore how LLMs, through LLMOps, are revolutionizing various sectors by enabling complex tasks that once required human intelligence. We’ll see how these models are embedded in digital applications, from virtual assistants to advanced media tools, becoming essential in our digital interactions.

In this chapter, we’re going to cover the following topics:

  • The evolution of NLP and LLMs
  • Traditional MLOps versus LLMOps
  • Trends in LLM integration
  • The core concepts of LLMOps
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