This article explains about how to do Inferencing of Log Amomaly Detection and Similar incidents in Watson AIOps.
The article is based on the the following
- RedHat OpenShift 4.8 on IBM Cloud (ROKS)
- Watson AIOps 3.3.0
1. Demo Script
Need to update few properties in the config.sh.
The demo script is avialable here.
1.1. Update API_URL Property
Update the API_URL property with application url.
API_URL=http://ilender-frontweb-ilender-ns.aaaaa.cloud
Refer : 1. Deploying iLender Application to know how the app is deployed.
1.2. Update API_URL_CREDIT_SCORE Url
Update the API_URL_CREDIT_SCORE property with creditscore service url.
API_URL_CREDIT_SCORE=http://ilender-creditscore-ilender-ns.aaaaa.cloud/creditscore
2. Run Demo (Inferencing)
This section explains about how to run the Inferencing demo in Watson AIOps.
2.1. Enable Data flow in Humio Integration
- Choose the
Humio integration from the Data and Tool integrations page.
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Enable the Data flow on.
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Select the option Live data for Continious AI training and anomaly detection.
-
Save it.
2.2. Enable Data flow in ServiceNow Integration
- Choose the
ServiceNow integration from the Data and Tool integrations page.
-
Enable the Data flow on.
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Select the option Live data for Continious ticket data collection.
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Save it.
2.3. Run Demo Script
The demo script is avialable here.
Run sh 01-demo.sh to start the demo.
You will see the menu options like this.
-
- Enter
1 to choose the menu option 1 - Create Loan in iLender App
The output would be like the below. This will run for 4 minutes.
This demo option will introduce out of memory error in the creditscore service based on the increasing load.
- As a result, the log anomaly is created and story will be created in the slack.
Next Step
By sucessful execution of the above demo step, the story would have been created and you can see them in the next section Inferencing - View Results.