Cloudera Docs

Managing AI Studios

The AI Studios feature within Cloudera AI Workbench provides users with a comprehensive interface for managing and interacting with AI Studio deployments. The functionality includes viewing available studios, monitoring deployment status, accessing embedded studio applications, and performing redeployment or resumption of studio workflows.

Accessing the AI Studios Catalog

To access the AI Studios catalog:

  1. In the Cloudera console, click the Cloudera AI tile.

    The Cloudera AI Workbenches page displays.

  2. Click on the name of the workbench.

    The workbenches Home page displays.

  3. Click Projects, and then select the required Project.

    In the left navigation pane, the new AI Studios option is displayed.

  4. Browse the available AI Studio templates listed in the catalog.

Viewing AI Studio Deployment Status

The AI Studios page provides visibility into the status of all AI Studio deployments associated with the current project. Each deployment is listed with its corresponding status, allowing users to monitor ongoing and completed activities.

Accessing the embedded AI Studios application

Once an AI Studio deployment has completed successfully, the associated embedded application will be available within the AI Studios sidebar. To launch the application:

  1. In the Cloudera console, click the Cloudera AI tile.

    The Cloudera AI Workbenches page displays.

  2. Click on the name of the workbench.

    The workbenches Home page displays.

  3. Click Projects, and then select the required Project.

    In the left navigation pane, the AI Studios option is displayed.

  4. Click on the desired studio entry to open the corresponding embedded application.

Redeploying or Resuming AI Studios

  • Redeploying an AI Studio

    Click to re-import and execute tasks based on updates made to the .project-metadata.yaml file. This is useful when changes have been made to the project configuration or workflow definitions.

  • Resuming an AI Studio

    Click to continue execution from the point of failure in the last deployment. This is particularly beneficial for recovering from interrupted or failed tasks without restarting the entire workflow.