Documentation is now available for the Fall 2020 Update release!

Managing Alert Escalation Policy

Describes how to edit or delete a policy and detailed information on ML status.

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Introduction

All the created policies appear on the ALERT ESCALATION POLICIES LIST page. Apart from creating a policy, you can edit, create a copy, and delete policies. You can also view the number of notifications sent or incidents created from a policy.

Editing an alert escalation policy

To modify the details of an exisiting configured escalation policy:

  1. Click All Clients, select a client.
  2. Go to Setup > Alerts > Alert Escalation.
  3. From ALERT ESCALATION POLICY DETAILS, click the desired policy name.
  4. Click Edit, modify the as per your requirement, and then click Save.
    The policy is updated.

Creating a copy of an alert escalation policy

You can create a copy of an existing escalation policy with a different name and mode.

To create a copy:

  1. Go to Setup > > Alerts > Alert Escalation.
  2. From ALERT ESCALATION POLICY DETAILS, click the desired policy name.
  3. Click Create a Copy icon.
    STEP 1: SELECT NAME AND SCOPE FOR NEW ALERT ESCALATION POLICY page is displayed.
  4. Modify the policy as per your requirement, and then click Save.

Deleting an escalation policy

To delete a policy:

  1. Go to Setup > Alerts > Alert Escalation.
  2. From ALERT ESCALATION POLICY DETAILS, click the desired policy name.
  3. Click Remove icon.
    The screen displays a confirmation message.
  4. Clcik Yes to continue.
    The policy is deleted.

Viewing ML Status

Machine Learning (ML) status helps you understand the various stages of machine learning implementation in a policy from analyzing a sequence to escalating alerts.

ML Status
ML StatusDescription
Insufficient data. The policy is temporarily disabled. Due to insufficient data, the machine learning model cannot detect the alert sequences, and escalation does not happen. Hence, the policy is temporarily disabled. The policy becomes active when the machine learning model finds sufficient data.
Training ML model is queued. To use, please wait for completion. When a policy is created or a CSV file is uploaded to a policy, the training can be queued. If already a policy is in training, the new policy is queued. Once the training on the existing policy is complete, the status of the new policy moves to Training Initiated.
Training ML model is initiated. To use, please wait for completion. Training on the machine learning model is initiated. The status then moves to Training Started.
Training ML model is started. To use, please wait for completion. Training on the machine learning model is started. The progress of the training is visible on the progress bar.
Training ML model is in progress. To use, please wait for completion. Training on the ML model is in progress. The percentage of progress is shown in the progress bar.
ML model training is complete. You are ready to benefit from predictions. Training the ML model is complete. The ML model detects the alert sequences and escalation happens.
ML training encountered an error. Please contact OpsRamp Support.