KFServing is an open-source project designed to simplify the deployment and management of machine learning models on Kubernetes. It provides a standardized way to serve models, enabling developers to easily expose their models as RESTful APIs. KFServing supports various frameworks, including TensorFlow, PyTorch, and Scikit-learn, allowing for flexibility in model selection. Key features include automatic scaling, rollout of new model versions, and integration with advanced capabilities like A/B testing and canary deployments. By leveraging Kubernetes’ orchestration capabilities, KFServing enhances the efficiency and reliability of serving machine learning models in production environments.