Enterprise LLMOps: Advancing Large Language Models Operations Practice

被引:0
作者
Shan, Richard
Shan, Tony
机构
来源
2024 IEEE CLOUD SUMMIT, CLOUD SUMMIT 2024 | 2024年
关键词
LLMs; LLMOps; framework; enabler; operations; lifecycle; deployment; CI/CD; management; enterprise; strategy; implementation; governance; collaboration; ethics;
D O I
10.1109/Cloud-Summit61220.2024.00030
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
This paper provides an in-depth exploration of Enterprise LLMOps, examining the cultivation and operational dynamics of Large Language Models (LLMs) within business contexts. It sheds light on the profound impact LLMs have across various industries, while thoughtfully navigating the associated technical, ethical, and regulatory complexities. At the heart of this study lies the LLMOps framework, an essential practice for managing the LLM lifecycle effectively, structured around the Discover, Distill, Deploy, and Deliver (4D) stages. The research meticulously discusses each element within this 4D framework, elucidating key enablers and providing practical guidelines for utilization. Empirical examples serve to ground the discussion in reality, offering insights into the real-world implementation of LLMOps and unveiling valuable strategies and learnings from successful enterprise applications. The analysis concludes with a forward-looking perspective, forecasting advancements in LLMOps discipline and the continual evolution of ethical considerations and standards.
引用
收藏
页码:143 / 148
页数:6
相关论文
共 10 条
[1]  
Arawjo I, 2024, Arxiv, DOI arXiv:2309.09128
[2]  
Auffarth B., Generative AI with LangChain: Build Large Language Model (LLM) Apps with Python, ChatGPT, and Other LLMs
[3]  
Duke T., Building Responsible AI Algorithms: A Framework for Transparency, Fairness, Safety, Privacy, and Robustness
[4]  
Jindal AK, 2024, Arxiv, DOI arXiv:2403.02247
[5]  
Kulkarni A., Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, ChatGPT, and Other LLMs
[6]  
McMahon A. P., Machine Learning Engineering with Python: Manage the Lifecycle of Machine Learning Models Using MLOps with Practical Examples
[7]  
Natarajan S., Multi-Cloud Handbook for Developers: Learn how to Design and Manage Cloud-native Applications in AWS, Azure, GCP, and More
[8]  
Ranganathan Chitra Sabapathy, DevOps and Micro Services
[9]  
Subhash Khandare S., Mastering Large Language Models: Advanced techniques, applications, cutting-edge methods, and top LLMs
[10]  
Wali R. S., Breaking the Language Barrier: Demystifying Language Models with OpenAI: Unraveling OpenAI Frameworks for Advanced Conversational AI [ChatGPT Included]