From c49f3a548d997064375477e13b2cd8eccbfbd77a Mon Sep 17 00:00:00 2001 From: Dan Sun Date: Sat, 14 Jan 2023 17:31:29 -0500 Subject: [PATCH] Update README.md (#2648) Signed-off-by: Dan Sun Signed-off-by: Dan Sun --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 50945925b07..6a733c363a5 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ [![OpenSSF Best Practices](https://bestpractices.coreinfrastructure.org/projects/6643/badge)](https://bestpractices.coreinfrastructure.org/projects/6643) [![Releases](https://img.shields.io/github/release-pre/kserve/kserve.svg?sort=semver)](https://github.com/kserve/kserve/releases) [![LICENSE](https://img.shields.io/github/license/kserve/kserve.svg)](https://github.com/kserve/kserve/blob/master/LICENSE) -[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://kubeflow.slack.com/join/shared_invite/zt-cpr020z4-PfcAue_2nw67~iIDy7maAQ) +[![Slack Status](https://img.shields.io/badge/slack-join_chat-white.svg?logo=slack&style=social)](https://kubeflow.slack.com/archives/CH6E58LNP) KServe provides a Kubernetes [Custom Resource Definition](https://kubernetes.io/docs/concepts/extend-kubernetes/api-extension/custom-resources/) 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.