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Accessing Machine Learning workspace in Cloudera Observability

Steps for accessing the Machine Learning workspace in Cloudera Observability to monitor all workspaces within the Machine Learning environment, individual workspace, ML workload performance by category (job, sessions, model, and application), and analyze workspace and resource usage.

  1. Verify that you are logged in to the Cloudera Observability web UI and that you selected an environment from the Analytics Environments page.  Show Me How
    1. In a supported browser, log into the Cloudera.
      The Cloudera web interface landing page opens.
    2. From the Your Enterprise Data Cloud landing page, select the Observability tile.
      The Cloudera Observability landing page opens to the main navigation panel.
    3. From the Cloudera Observability Environments page, select the environment required for analysis.

      The Environment navigation panel opens.

  2. Verify that the Active System Monitoring link is highlighted in the main navigation panel.
  3. On the Environments page, from the Environments list, select the Cloudera AI Workbench environment.
    A list of ML workspaces and their current versions are displayed.
  4. Click the environment name.
  5. From the ML summary dashboard, monitor all workspaces within the Cloudera AI Workbench environment and analyze workspace and resource usage.
  6. In the Workspace Usage Analysis section, from the active workspaces list, click the workspace name link to monitor an individual workspace and analyze resource usage by nodes.
    Additionally, you can monitor ML workload performance metrics by category (job, sessions, model, and application).