KF Serving provides a Custom Resource Definition for serving ML Models on arbitrary frameworks. It aims to solve 80% of model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and Custom containers.
A KFService encapsulates the complexity of autoscaling, networking, health checking, server configuration, and more, to provide customers with a simple and seamless experience when deploying models.
In the future, we hope to support more advanced use cases such as skew detection, explainability, and performance profiling across infrastructure configurations.