Chapter 1. Using data science projects 1.1. Creating a data science project To implement a data science workflow, you must create a project. In OpenShift, a project is a Kubernetes namespace with additional annotations, and is the main way that you can manage user access to resources. A project organizes your data science work in one place and also allows you to collaborate with other developers and data scientists in your organization. Within a project, you can add the following functionality: Connections so that you can access data without having to hardcode information like endpoints or credentials. Workbenches for working with and processing data, and for developing models. Deployed models so that you can test them and then integrate them into intelligent applications. Deploying a model makes it available as a service that you can access by using an API. Pipelines for automating your ML workflow. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have the appropriate roles and permissions to create projects. Procedure From the OpenShift AI dashboard, select Data Science Projects . The Data Science Projects page shows a list of projects that you can access. For each user-requested project in the list, the Name column shows the project display name, the user who requested the project, and the project description. Click Create project . In the Create project dialog, update the Name field to enter a unique display name for your project. Optional: If you want to change the default resource name for your project, click Edit resource name . The resource name is what your resource is labeled in OpenShift. Valid characters include lowercase letters, numbers, and hyphens (-). The resource name cannot exceed 30 characters, and it must start with a letter and end with a letter or number. Note: You cannot change the resource name after the project is created. You can edit only the display name and the description. Optional: In the Description field, provide a project description. Click Create . Verification A project details page opens. From this page, you can add connections, create workbenches, configure pipelines, and deploy models. 1.2. Updating a data science project You can update the project details by changing the project name and description. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users or rhoai-admins ) in OpenShift. You have created a data science project. Procedure From the OpenShift AI dashboard, click Data Science Projects . The Data Science Projects page opens. Click the action menu ( ... ) beside the project whose details you want to update and click Edit project . The Edit project dialog opens. Optional: Edit the Name field to change the display name for your project. Optional: Edit the Description field to change the description of your project. Click Update . Verification You can see the updated project details on the Data Science Projects page. 1.3. Deleting a data science project You can delete data science projects so that they do not appear on the OpenShift AI Data Science Projects page when you no longer want to use them. Prerequisites You have logged in to Red Hat OpenShift AI. If you are using OpenShift AI groups, you are part of the user group or admin group (for example, rhoai-users ) in OpenShift. You have created a data science project. Procedure From the OpenShift AI dashboard, click Data Science Projects . The Data Science Projects page opens. Click the action menu ( ... ) beside the project that you want to delete and then click Delete project . The Delete project dialog opens. Enter the project name in the text field to confirm that you intend to delete it. Click Delete project . Verification The data science project that you deleted is no longer displayed on the Data Science Projects page. Deleting a data science project deletes any associated workbenches, data science pipelines, cluster storage, and connections. This data is permanently deleted and is not recoverable.

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