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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

Securing and governing models with LLMOps

In the context of LLMs, the intersection of security and governance is a domain of growing importance. The Open Web Application Security Project (OWASP) has identified the top 10 risks specifically for LLMs, providing a structured approach for mitigating potential threats that these advanced systems face. Addressing these risks through effective governance strategies is essential for establishing a secure, transparent, and accountable artificial intelligence (AI) infrastructure within an organization.

Managing OWASP risks in LLMs

The OWASP highlights several risks particular to LLMs, requiring rigorous strategies to mitigate these issues effectively:

  • Prompt injection:

    To safeguard against prompt injections, which are manipulative inputs designed to deceive LLMs, organizations must implement comprehensive input validation measures. Techniques such as setting input length restrictions, filtering out special characters, and utilizing...

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