KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX.

It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. It enables a simple, pluggable, and complete story for Production ML Serving including prediction, pre-processing, post-processing and explainability. KServe is being used across various organizations.

For more details, visit KServe website

Since 0.7 KFServing is rebranded to KServe, we still support the RTS release 0.6.x, please refer to corresponding release branch for docs.

• KServe is a standard, cloud agnostic Model Inference Platform on Kubernetes, built for highly scalable use cases.
• Provides performant, standardized inference protocol across ML frameworks.
• Support modern serverless inference workload with request based autoscaling including scale-to-zero on CPU and GPU.
• Provides high scalability, density packing and intelligent routing using ModelMesh.
• Simple and pluggable production serving for inference, pre/post processing, monitoring and explainability.
• Advanced deployments for canary rollout, pipeline, ensembles with InferenceGraph.

To learn more about KServe, how to use various supported features, and how to participate in the KServe community, please follow the KServe website documentation. Additionally, we have compiled a list of presentations and demoes to dive through various details.

• Serverless Installation: KServe by default installs Knative for serverless deployment for InferenceService.
• Raw Deployment Installation: Comparing to Serverless Installation, it is more lightweight installation however this option does not support canary deployment and request based autoscaling with scale-to-zero.
• ModelMesh Installation: You can optionally install ModelMesh to enable high-scale, high-density and frequently-changing model serving use cases.
• Quick Installation: Install KServe on your local machine.

KServe is an important addon component of Kubeflow, please learn more from the Kubeflow KServe documentation and follow KServe with Kubeflow on AWS to learn how to use KServe on AWS.