AIOps

Hybrid IT environments produce an immense amount of data, that can pose a difficult management challenge for even the most skilled ITOps teams.  But with these challenges also comes opportunity.  Patterns hidden within these data can reveal opportunities to automate, but you need the tools to help you parse it.

A great way to detect these patterns is to first detect anomalies in the data. Typically, organizations set either static thresholds or manual rules to define and manage deviations from normal behavior. The problem with status thresholds is that first, it takes a long time for subject matter experts (SME) to distill them from their experience and to create them and second, they don't adapt to changes and therefore, tend to get outdated and irrelevant quickly. Operations teams waste time and effort in managing these thresholds and end up missing important clues.  

Join IBM Fellow and CTO of AI for IT, Rama Akkiraju as she chats with Anbang Xu, the Chief Architect of the anomaly detection and action remediation functionality built into Watson AIOps, about how Watson AIOps was designed to leverage anomaly detection from both metrics and logs, and how you can integrate this powerful tool into your own ITOps strategy.


Tune in with @rama akkiraju and @anbang xu in this on demand webinar, Anomaly prediction with Watson AIOps. Post any of your questions below and you can watch the on demand recording here. 

Thanks, 

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Michael Buss
Sign up for IBM Cloud Pak Demo Days Series : http://ibm.biz/CloudPakDemoDays
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Hi everyone, 
Check out the on demand recording here and share your questions below! 

The slides can be downloaded here. 

Thanks, 

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Michael Buss
Sign up for IBM Cloud Pak Demo Days Series : http://ibm.biz/CloudPakDemoDays
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Original Message:
Sent: Thu October 01, 2020 05:25 PM
From: Michael Buss
Subject: Anomaly prediction with Watson AIOps

Hybrid IT environments produce an immense amount of data, that can pose a difficult management challenge for even the most skilled ITOps teams.  But with these challenges also comes opportunity.  Patterns hidden within these data can reveal opportunities to automate, but you need the tools to help you parse it.

A great way to detect these patterns is to first detect anomalies in the data. Typically, organizations set either static thresholds or manual rules to define and manage deviations from normal behavior. The problem with status thresholds is that first, it takes a long time for subject matter experts (SME) to distill them from their experience and to create them and second, they don't adapt to changes and therefore, tend to get outdated and irrelevant quickly. Operations teams waste time and effort in managing these thresholds and end up missing important clues.  

Join IBM Fellow and CTO of AI for IT, Rama Akkiraju as she chats with Anbang Xu, the Chief Architect of the anomaly detection and action remediation functionality built into Watson AIOps, about how Watson AIOps was designed to leverage anomaly detection from both metrics and logs, and how you can integrate this powerful tool into your own ITOps strategy.


Tune in with @rama akkiraju and @anbang xu in this on demand webinar, Anomaly prediction with Watson AIOps. Post any of your questions below and you can watch the on demand recording here. 

Thanks, 

​​

------------------------------
Michael Buss
Sign up for IBM Cloud Pak Demo Days Series : http://ibm.biz/CloudPakDemoDays
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