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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

Operationalizing compliance and performance

Operationalizing performance, security, governance, legal, and regulatory compliance for LLMs involves creating a comprehensive system that spans various departments and functions within an organization. Integrating robust workflows, such as those managed by Apache Airflow, with strategic human review points, and ensuring compliance with data and model licensing, are key components of this system.

Operationalizing performance

For performance, establishing continuous integration/continuous deployment (CI/CD) pipelines managed by workflow orchestration tools such as Apache Airflow ensures that models are consistently evaluated against performance benchmarks. Directed acyclic graphs (DAGs) in Airflow can be programmed to automatically trigger performance evaluation tasks, such as running test suites that measure the LLM’s accuracy, recall, precision, and F1 scores against a validation set.

These DAGs can also include steps for...

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