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Large Language Model Operations (LLMOps) is a methodology focused on managing, deploying, monitoring, and maintaining Large Language Models (LLMs) in production environments. LLMOps is an extension of the Machine Learning Operations (MLOps) framework, which itself is an extension of DevOps. LLMOps addresses the unique challenges associated with LLMs like OpenAI’s GPT series, Google’s Gemini, and Anthropic’s Claude. It has gained popularity since early 2023 as businesses began exploring generative AI deployments.
The main goal of LLMOps is to ensure that LLMs are reliable, efficient, and scalable when integrated into real-world applications. It offers benefits such as flexibility, automation, collaboration, performance improvement, and security and ethics considerations. The LLMOps lifecycle includes stages like data collection and preparation, model training or fine-tuning, model testing and validation, deployment, and optimization and maintenance.
Compared to MLOps, LLMOps focuses specifically on managing LLMs, which are resource-intensive and require specialized infrastructure like GPUs or TPUs. LLMOps also faces challenges related to the development process, visibility and interpretability, ethics, security, compliance considerations, operational and infrastructure requirements, and scale and complexity. The differences between MLOps and LLMOps lie in the specific focus on LLMs in LLMOps and the unique challenges associated with managing these large language models in real-world applications.
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Source link: https://www.techtarget.com/searchenterpriseai/definition/large-language-model-operations-LLMOps
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