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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

Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835887509

Length 190 pages

Edition 1st Edition

Languages

Python

Tools

Azure

Concepts

Artificial Intelligence

Author (1):

Ryan Doan

View More author details

Table of Contents (14) Chapters

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

Why subscribe?

13. Other Books You May Enjoy

Continuous improvement

In the realm of LLMs such as GPT, continuous improvement is paramount to maintaining their relevance and efficacy. This process is multifaceted and involves integrating new data, developing strategies to prevent previous knowledge from being forgotten, and incorporating human feedback.

Firstly, incorporating newer data is essential as language and societal contexts evolve. Regular updates with recent datasets ensure that models grasp current terminologies, phrases, and relevant topics. This could involve data from diverse sources, such as the latest news articles, scientific publications, and trending internet content. Such updates help the model stay current with linguistic trends and societal changes.

Secondly, a significant challenge in training neural networks, especially when they learn new information, is avoiding catastrophic forgetting. This phenomenon occurs when a model loses the information it had learned previously upon acquiring new knowledge...

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Authors (1)

Ryan Doan

Ryan Doan

Ryan Doan is a former ML engineer at Amazon and currently serves as the VP of technology at Semantic Health. He is also a private equity investor, focusing on Software-as-a-Service SaaS-based AI businesses, and the founder of MLExpert, a technical interview preparation course with over 60,000 students. Ryan has leveraged his technical expertise to develop machine learning models for diverse sectors, including trading firms, political campaigns, and government organizations. Most recently, he's spent three years at Semantic Health, which was acquired by AAPC in 2023. During this time, he led the development of large language models (LLM) applications that significantly enhanced revenue cycle management for hospitals in the US and Canada. In this book, Ryan shares what he learned from integrating language models and their operations into organizations, drawing on his broad experience to provide valuable insights into the effective use of these technologies. I'm very grateful to my brothers and parents for their unwavering support and encouragement throughout all my ventures, no matter how challenging or outlandish they seem.

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