Chapter 3. 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.
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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.
