The Ray kubectl plugin simplifies the process of deploying and managing Ray clusters on Kubernetes, making it easier for data scientists and AI researchers to get started with Ray in a Kubernetes environment. The plugin streamlines tasks such as creating and managing Ray clusters, submitting jobs, and querying cluster information. With KubeRay v1.3, the Ray kubectl plugin has been promoted to beta, bringing improved stability and new commands that enhance the overall user experience. Users can now create Ray clusters with default values in place, add worker groups, and use the `create workergroup` command to manage their Ray clusters more efficiently. The plugin also supports automatic reconnection for Ray sessions, capturing detailed logs for each Ray type, and making it easier to debug issues. Additionally, users can submit Ray jobs on the fly and delete resources with ease. By combining the Ray kubectl plugin with KubeRay, developers can unlock new possibilities for their AI workloads and take advantage of the powerful combination of seamless development experience and robust orchestration provided by Kubernetes.