Model Deployment

Model deployment refers to the process of making a trained machine learning model available for use in a production environment. This involves integrating the model into an existing application or system so that it can take in new data and provide predictions or insights in real-time. Key characteristics of model deployment include scalability, reliability, and accessibility, ensuring that the model can handle varying loads and is available to users as needed. Common use cases for model deployment include web applications, mobile apps, and enterprise software where automated decision-making processes are required.