Machine learning operations (MLOps) have been foundational for efficient AI development, facilitating the deployment of machine learning models through continuous testing, updating, and monitoring. As AI applications evolve, particularly with the rise of large language models (LLMs), there's a shift toward large language model operations (LLMOps), which adapt MLOps principles to meet the unique demands of LLMs. LLMOps is not just about managing these models, but also about their orchestration, governance, and optimization, enabling advanced applications like chatbots and content generation tools to leverage LLMs' linguistic capabilities. It addresses challenges such as the need for enhanced infrastructure, sophisticated version control, real-time language understanding, and model interpretability. While MLOps provides a versatile framework for diverse applications, LLMOps specifically targets the scalability and complexity of language-driven AI, utilizing continuous integration/continuous delivery (CI/CD) practices to streamline updates and improvements. As AI continues to shape the technological landscape, integrating LLMOps is crucial for developers seeking to optimize and maintain high-performing AI applications.