Serving frameworks are specialized tools designed to facilitate the deployment and management of machine learning models in production environments. They streamline the process of serving predictions from models, ensuring that they can handle requests efficiently and at scale. These frameworks often provide features such as load balancing, version control, and monitoring, which are essential for maintaining the performance and reliability of machine learning applications. By utilizing serving frameworks, developers can focus on building and improving their models while ensuring that the infrastructure for serving predictions is robust and efficient.