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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
2. Chapter 1: Introduction to LLMs and LLMOps FREE CHAPTER 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

Ensuring legal and regulatory compliance

Navigating the legal landscape of AI, particularly with regard to LLMs, presents a complex array of challenges that require a strategic partnership between IT, security, and legal departments. This alliance is essential for pinpointing legal ambiguities and formulating responses to the evolving nature of AI technology.

Product warranties within AI development must be explicit, clearly designating responsibility for AI-driven outcomes. This ensures clarity in who is liable should an AI system malfunction or cause damage. It’s crucial to re-examine terms and conditions in user agreements, especially those related to generative AI platforms. Such revisions must address how user prompts are handled, rights over outputs, ownership issues, data privacy concerns, and limitations on the use of AI-generated content.

End-user license agreements (EULAs) must be carefully crafted to protect the organization against liabilities that could arise...

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