Definition
Model deployment is the process of making a trained artificial intelligence model available for use in a production environment. This involves integrating the model into existing software systems or applications so it can process new data and generate predictions or insights. It is a critical step in the AI lifecycle, transforming a theoretical model into a practical tool that delivers business value. For instance, a deployed fraud detection model can analyze transactions in real-time to identify suspicious activity. This differs from model training, which focuses on developing the model's accuracy using historical data. Understanding model deployment is essential for roles involved in AI operations and MLOps, and it appears in advanced AI and cloud certifications.