Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models
Large Language Models (LLMs), such as the GPT series, LLaMA, and BERT, possess incredible capabilities in human-like text generation and understanding across diverse domains, which have revolutionized artificial intelligence applications. However, their operational complexity necessitates a speciali...
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MDPI AG
2025-01-01
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| author | Saurabh Pahune Zahid Akhtar |
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| description | Large Language Models (LLMs), such as the GPT series, LLaMA, and BERT, possess incredible capabilities in human-like text generation and understanding across diverse domains, which have revolutionized artificial intelligence applications. However, their operational complexity necessitates a specialized framework known as LLMOps (Large Language Model Operations), which refers to the practices and tools used to manage lifecycle processes, including model fine-tuning, deployment, and LLMs monitoring. LLMOps is a subcategory of the broader concept of MLOps (Machine Learning Operations), which is the practice of automating and managing the lifecycle of ML models. LLM landscapes are currently composed of platforms (e.g., Vertex AI) to manage end-to-end deployment solutions and frameworks (e.g., LangChain) to customize LLMs integration and application development. This paper attempts to understand the key differences between LLMOps and MLOps, highlighting their unique challenges, infrastructure requirements, and methodologies. The paper explores the distinction between traditional ML workflows and those required for LLMs to emphasize security concerns, scalability, and ethical considerations. Fundamental platforms, tools, and emerging trends in LLMOps are evaluated to offer actionable information for practitioners. Finally, the paper presents future potential trends for LLMOps by focusing on its critical role in optimizing LLMs for production use in fields such as healthcare, finance, and cybersecurity. |
| format | Article |
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| institution | DOAJ |
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| language | English |
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| spelling | doaj-art-20a41231a8a24a0180372d9c8ce7dc762025-08-20T03:12:19ZengMDPI AGInformation2078-24892025-01-011628710.3390/info16020087Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language ModelsSaurabh Pahune0Zahid Akhtar1Cardinal Health, Dublin, OH 43017, USADepartment of Network and Computer Security, State University of New York Polytechnic Institute, Utica, NY 13502, USALarge Language Models (LLMs), such as the GPT series, LLaMA, and BERT, possess incredible capabilities in human-like text generation and understanding across diverse domains, which have revolutionized artificial intelligence applications. However, their operational complexity necessitates a specialized framework known as LLMOps (Large Language Model Operations), which refers to the practices and tools used to manage lifecycle processes, including model fine-tuning, deployment, and LLMs monitoring. LLMOps is a subcategory of the broader concept of MLOps (Machine Learning Operations), which is the practice of automating and managing the lifecycle of ML models. LLM landscapes are currently composed of platforms (e.g., Vertex AI) to manage end-to-end deployment solutions and frameworks (e.g., LangChain) to customize LLMs integration and application development. This paper attempts to understand the key differences between LLMOps and MLOps, highlighting their unique challenges, infrastructure requirements, and methodologies. The paper explores the distinction between traditional ML workflows and those required for LLMs to emphasize security concerns, scalability, and ethical considerations. Fundamental platforms, tools, and emerging trends in LLMOps are evaluated to offer actionable information for practitioners. Finally, the paper presents future potential trends for LLMOps by focusing on its critical role in optimizing LLMs for production use in fields such as healthcare, finance, and cybersecurity.https://www.mdpi.com/2078-2489/16/2/87large language models (LLMs)LLMOpsMLOpsmodel fine-tuninginfrastructure scalabilityethical AI practices |
| spellingShingle | Saurabh Pahune Zahid Akhtar Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models Information large language models (LLMs) LLMOps MLOps model fine-tuning infrastructure scalability ethical AI practices |
| title | Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models |
| title_full | Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models |
| title_fullStr | Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models |
| title_full_unstemmed | Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models |
| title_short | Transitioning from MLOps to LLMOps: Navigating the Unique Challenges of Large Language Models |
| title_sort | transitioning from mlops to llmops navigating the unique challenges of large language models |
| topic | large language models (LLMs) LLMOps MLOps model fine-tuning infrastructure scalability ethical AI practices |
| url | https://www.mdpi.com/2078-2489/16/2/87 |
| work_keys_str_mv | AT saurabhpahune transitioningfrommlopstollmopsnavigatingtheuniquechallengesoflargelanguagemodels AT zahidakhtar transitioningfrommlopstollmopsnavigatingtheuniquechallengesoflargelanguagemodels |