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:
- Data 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 specialized OpenShift AI groups, you are part of the user group or admin group (for example,
rhoai-usersorrhoai-admins) in OpenShift.
Procedure
- From the OpenShift AI dashboard, select Data Science Projects.
- Click Create data science project.
- In the Create a data science project dialog, enter a display Name for your project.
Optional: Edit the Resource name for your data science project. The resource name must consist of lowercase alphanumeric characters, -, and must start and end with an alphanumeric character.
Note: After you create a project, you can change the project display name but you cannot change the resource name.
- Enter a description for your data science project.
- Click Create.
Verification
- A project details page opens. From this page, you can add data connections, create workbenches, configure pipelines, and deploy models.
1.2. Updating a data science project
You can update your data science project’s details by changing your project’s name and description text.
Prerequisites
- You have logged in to Red Hat OpenShift AI.
-
If you are using specialized OpenShift AI groups, you are part of the user group or admin group (for example,
rhoai-usersorrhoai-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 data science project dialog opens.
- Optional: Update the name for your data science project.
- Optional: Update the description for your data science project.
- Click Update.
Verification
- The data science project that you updated is displayed 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 specialized OpenShift AI groups, you are part of the user group or admin group (for example,
{oai-user-group}) 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 data connections. This data is permanently deleted and is not recoverable.
