OpsLogix is happy to announce the latest update of the EZalert solution, version 2. It does a lot more than just closing alerts, in comparison to the first release of the solution.

So What’s New With EZalert?

EZalert is a tool that uses machine learning to help you manage your SCOM environment in a more efficient and effective way, it helps you filter out noisy alerts and lower the total cost of ownership.

When starting out with EZalert, you will have to start “training” EZalert to handle new incoming alerts. By setting the resolution state on new incoming alerts EZalert “learns” what resolution state you would like to set for the same or similar alerts when a new alert is generated.

Eventually, with enough training, EZalert will start predicting with an increasingly higher accuracy what resolution state to set for an alert. When you are confident that EZalert predicts the resolution state for incoming alerts accurately, you can turn on auto apply and EZalert will automatically apply the predicted resolution state to the new incoming alerts in real-time. Not only is it possible to let EZalert set the resolution state on new incoming alerts, but you can also attach a PowerShell script (and use properties of the alert as parameters) to a specific resolution state as an action.

 

Watch The Demo Here

QuickStart Guide

Mandatory
  1.  Configure your resolution states based on where your alerts should be transferred to. This could be different groups within your IT-organization that should work with the alerts.
  2. Train your model with all incoming alerts to match your desired resolution state. Consider closing alerts (255) that you might use for reporting purpose that doesn’t require immediate attention to the cleanup noise.
  3. Don’t train simple alerts with too many entries when the confidence level is high. This will only slow down the training and consume more memory.
  4. Enable auto-apply. We recommend a confidence level above 85% in order to get good results. This will also ensure that alerts that don’t match won’t be forwarded to a trained resolution state.
  5. Use the low confidence filter to find the alerts that need more training and train them until they reach the configured confidence level.
Optional
  1. Pre-actions to set Custom fields for example Management Pack, Operated By – used by the machine learning algorithm.
  2. Post actions Custom fields for a statistical purpose on what resolution state was applied.