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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

Selecting the foundation model

Selecting the right foundation model for your text generation task is a pivotal decision that shapes the capabilities, efficiency, and scalability of your LLM application. With the advent of models like Mistral, Llama2, and Gemma, the landscape of foundation models has expanded, offering tailored solutions for a wide array of applications.

This section explores the considerations for choosing between these cutting-edge models.

Choosing the LLM for your specific use case

The foundational step in leveraging AI for any task is to precisely define the desired outcome. This clarity is crucial as it directly influences the selection of an appropriate AI model by pinpointing the exact features needed for success, including nuanced language comprehension, creative output generation, and the ability to personalize content. By focusing on models available on platforms such as Hugging Face that are pre-aligned with these specific requirements, this step...

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