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 users to easily deploy, scale, and manage their machine learning inference workloads. KFServing supports various frameworks, including TensorFlow, PyTorch, and Scikit-learn, allowing for flexibility in model selection. With features like automatic scaling, rollout of new model versions, and support for A/B testing, KFServing enhances the operational efficiency of machine learning applications. By leveraging Kubernetes’ capabilities, it ensures high availability and reliability for serving models in production environments.