LLMOps: Operationalizing Language Models for the Enterprise

Suruchi Shah

doi:10.32628/CSEIT241051044

Authors

Suruchi Shah LinkedIn, USA Author

DOI:

https://doi.org/10.32628/CSEIT241051044

Keywords:

LLMOps, Language Models, AI Deployment, Model Lifecycle, Enterprise AI

Abstract

This comprehensive article explores the emerging field of LLMOps (Language Model Operations), a specialized branch of MLOps focused on deploying and managing large language models (LLMs) in production environments. The article examines the LLM lifecycle, challenges in scaling LLMs, essential LLMOps tooling, best practices for effective management, and future trends. Drawing on recent industry studies and statistics, it highlights the growing significance of LLMOps in enterprise AI applications, its potential to streamline operations and reduce costs, and its role in addressing critical issues such as model performance, scalability, data privacy, and ethical AI deployment

Downloads

Download data is not yet available.

References

Stanford University Institute for Human-Centered Artificial Intelligence, "Artificial Intelligence Index Report 2023," 2023. [Online]. Available: https://aiindex.stanford.edu/wp-content/uploads/2023/04/HAI_AI-Index-Report_2023.pdf

S. Ransbotham et al., "Winning With AI," MIT Sloan Management Review, 2019. [Online]. Available: https://sloanreview.mit.edu/projects/winning-with-ai/

L. Weng et al., "Holistic Evaluation of Language Models," 2022. [Online]. Available: https://arxiv.org/abs/2211.09110

OpenAI, "GPT-4 Technical Report," 2023. [Online]. Available: https://arxiv.org/abs/2303.08774

MIT Technology Review Insights, "The global AI agenda: Promise, reality, and a future of data sharing," 2020. [Online]. Available: https://www.technologyreview.com/2020/03/26/950287/the-global-ai-agenda-promise-reality-and-a-future-of-data-sharing/

D. Narayanan et al., "Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM," in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21), 2021. [Online]. Available: https://dl.acm.org/doi/10.1145/3458817.3476209 DOI: https://doi.org/10.1145/3458817.3476209

Gartner, "Market Guide for AI Trust, Risk and Security Management," 2023. [Online]. Available: https://www.gartner.com/en/documents/4021725

Datadog, "The State of Serverless," 2023. [Online]. Available: https://www.datadoghq.com/state-of-serverless/

R. Bommasani et al., "On the Opportunities and Risks of Foundation Models," arXiv preprint arXiv:2108.07258, 2021. [Online]. Available: https://arxiv.org/abs/2108.07258

T. Fountaine, B. McCarthy, and T. Saleh, "Building the AI-Powered Organization," Harvard Business Review, vol. 97, no. 4, pp. 62-73, 2019. [Online]. Available: https://hbr.org/2019/07/building-the-ai-powered-organization

IDC, "Worldwide Artificial Intelligence Software Forecast, 2023–2027," IDC, Sep. 2022. [Online]. Available: https://www.idc.com/getdoc.jsp?containerId=US50027023