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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
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

Developing talent and skill

As LLM technology continues to advance, the demand for specialized skill sets extends beyond traditional boundaries of AI and machine learning into areas that require a combination of technical acumen and interdisciplinary expertise. The evolving landscape of LLMs necessitates roles such as synthetic data generation specialists, prompt engineers, and policy optimizers, each contributing uniquely to the development and implementation of these sophisticated systems.

As data privacy concerns and the availability of large-scale training datasets continue to pose challenges, the role of synthetic data generation specialists becomes increasingly critical. These professionals are tasked with designing methods to generate artificial data that can safely and effectively train LLMs without compromising real data’s integrity or privacy. This role requires a deep understanding of both machine learning algorithms and data privacy laws, ensuring that synthetic...

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