LLMOps
LLMOps (Large Language Model Operations) is the set of practices, processes, and tools used to develop, deploy, monitor, maintain, and optimize large language model applications in production.
What is LLMOps?
LLMOps extends traditional machine learning operations (MLOps) to address the specific requirements of LLM-based systems. It covers activities such as model and prompt management, evaluation, deployment, monitoring, versioning, data management, and performance optimization. LLMOps also helps teams manage interactions between LLMs and components such as retrieval systems, APIs, tools, and AI agents.
Why is LLMOps Important?
LLM applications can involve complex workflows and changing models, prompts, and data sources. Effective LLMOps practices help organizations maintain reliability, control costs, identify performance or security issues, and continuously improve applications after deployment. It also provides greater visibility and governance across the LLM lifecycle.
Common use cases
LLMOps is commonly used for LLM deployment, model evaluation, prompt management, AI observability, RAG applications, AI agents, model monitoring, and enterprise generative AI.