Spot unusual application behavior with machine-learning help
Also listed as: ML-assisted application monitoring
What is it?
Xelence uses machine learning, a form of AI that learns normal patterns from data, to help flag unusual application behavior for support teams to look at.
Why does it matter?
Earlier warning of problems, so teams can act before users are affected.
Where people stay in control
Support teams review the flagged behavior and decide what action to take.
Start thinking about
- What service levels have you committed to, and how do you measure them today?
- Who responds when performance drops, and how are they alerted?
- Which monitoring data could contain personal information, and who should be allowed to view it?
- Should Xelence monitoring feed into the operations tools your agency already uses?
- How will you capture a performance baseline before an upgrade so you can compare afterwards?
Terms used on this card
- Application performance monitoring (APM)
- Tools that measure how an application is performing, such as speed, errors and load, so problems are found before users notice.
- Machine learning
- A form of AI that learns normal patterns from data, so it can flag what is unusual.