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7. LLMs for Enterprise and LLMOps

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Akshay Kulkarni
Adarsha Shivananda
Anoosh Kulkarni
Dilip Gudivada
Published in
Applied Generative AI for Beginners
Publisher
Apress

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Abstract

This chapter introduces a reference framework for the emerging app stack of large language models (LLMs), highlighting prevalent systems, tools, and design approaches used by AI startups and enterprises. It delves into the nascent stages of LLMs and their transformative potential, emphasizing responsible and ethical usage in enterprise settings. The framework centers around in-context learning, a prevalent design strategy that leverages off-the-shelf LLMs without fine-tuning. It also discusses the role of cloud services in enabling the full potential of LLMs, offering scalability, cost-efficiency, security, and seamless integration with existing workflows. Additionally, the chapter explores the challenges and benefits of fine-tuning LLMs for specific tasks and the importance of establishing a dedicated LLM testbed for research and development. It concludes by emphasizing the need for continuous monitoring and validation of LLM outputs to ensure their quality, reliability, safety, and ethical use.
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previous chapter Implement LLMs Using Sklearn
next chapter Diffusion Model and Generative AI for Images
Title
LLMs for Enterprise and LLMOps
Authors
Akshay Kulkarni
Adarsha Shivananda
Anoosh Kulkarni
Dilip Gudivada
Copyright Year
2023
Publisher
Apress
Book
Applied Generative AI for Beginners

Print ISBN: 978-1-4842-9993-7

Electronic ISBN: 978-1-4842-9994-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-1-4842-9994-4_7

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