LLMOps, similar to MLOps but focused on large language models, involves practices, processes, and tools aimed at simplifying the lifecycle management of these models. This includes model training, deployment, monitoring, and maintenance. It emphasizes efficiency in scaling large models, managing high processing demands, and ensuring model performance and reliability. Key challenges include version control, reproducibility, and resource optimization, which are addressed by systems like Kubernetes and tools such as TensorFlow Extended.