More than two years ago, the Zebrium UI team embarked on designing an experience around our Machine Learning (ML) engine, which was built to find the root cause of critical issues in logs so users wouldn't have to ... and the root cause experience was born!
More than two years ago, the Zebrium UI team embarked on designing an experience around our Machine Learning (ML) engine, which was built to find the root cause of critical issues in logs so users wouldn't have to. We knew our approach could cut Mean Time to Resolution (MTTR) from hours to minutes. But the idea of ML was foreign to users coming from the logging world, people who often relied on clever manual searching to do their jobs. To make them comfortable, our UI was built as a log viewer with ML features added around it. We had good success with that approach. But the utility of our UI was often measured by the quality of our search capability, not the quality of our ML. It wasn't until we focused the UI on what we do best, root cause detection through ML, that our users fully understood our value proposition... and the root cause experience was born!
