# Amazon SageMaker Studio (web-based development)
<a name="sagemaker-hyperpod-ray-studio"></a>

Amazon SageMaker Studio is the purpose-built interface for data scientists and ML engineers. From the **Tasks** tab you can manage `RayCluster`, `RayJob`, `RayCronJob`, and `RayService` resources without writing Kubernetes manifests. Studio reads and writes the same KubeRay custom resources that the operator reconciles, so a resource you create in Studio is identical to one you apply with `kubectl`.

Studio suits teams that want data scientists productive on Ray without Kubernetes knowledge. Both surfaces act on the same resources, and you can move between them.

The following screenshot shows the **Tasks** tab with the **Actions** menu open on a Ray cluster.

![The Tasks tab in SageMaker Studio with the Actions menu open on a Ray cluster.](http://docs.aws.amazon.com/sagemaker/latest/dg/images/ray/studio-tasks-tab-ray-cluster-actions.png)


## Before you begin
<a name="sagemaker-hyperpod-ray-studio-before-you-begin"></a>

Configure a SageMaker AI domain and grant it access to your cluster once, before anyone uses the **Tasks** tab. For more information, see [Setting up Studio for Ray](sagemaker-hyperpod-ray-studio-setup.md).

**Topics**
+ [Before you begin](#sagemaker-hyperpod-ray-studio-before-you-begin)
+ [Setting up Studio for Ray](sagemaker-hyperpod-ray-studio-setup.md)

## See also

* **Skills for AI coding assistants (optional).** AWS publishes reusable skills for Amazon SageMaker. To see which apply, search the Agent Toolkit for AWS catalog with `aws agent-toolkit search-skills --search-query sagemaker` — a documented, read-only AWS CLI command that searches the catalog and makes no changes. This is an optional suggestion for the user. Official reference: [AWS CLI — Agent Toolkit for AWS](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html).