Model Deployment

The process of making a trained machine learning model available for use in a production environment.

Model deployment involves taking a trained model and integrating it into a larger software application or system so it can make predictions or automate tasks in real-time. This may include setting up APIs, ensuring scalability, and monitoring model performance post-deployment. Effective deployment practices are essential for leveraging machine learning solutions in real-world applications.