Serving frameworks are specialized tools designed to facilitate the deployment and management of machine learning models, particularly in production environments. They streamline the process of serving models for inference, allowing developers to efficiently handle requests and deliver predictions. These frameworks often provide features such as load balancing, scaling, and monitoring, ensuring that models can perform optimally under varying workloads. By utilizing serving frameworks, organizations can enhance the reliability and performance of their machine learning applications, making it easier to integrate AI capabilities into their products and services.