Definition
LLM deployment refers to the process of making a trained large language model available for use in a production environment. This involves integrating the model into existing software, applications, or workflows so that users or other systems can interact with it. Deployment ensures that the capabilities of the LLM, such as text generation or analysis, can be leveraged to solve real-world problems. It typically includes steps like model packaging, API creation, infrastructure provisioning, and ongoing monitoring for performance and errors. This is crucial for realizing the value of AI investments and improving operational efficiency. For example, a customer service chatbot powered by an LLM would be considered a deployed model. LLM deployment is distinct from model training, which focuses solely on developing the model's intelligence. You will encounter LLM deployment concepts on AI-focused certification exams and within responsible AI programs.