Today's applications are evolving faster, growing more complex and failing in new ways. Although most organizations are able to tell when an application breaks, finding the root cause typically involves the time-consuming task of hunting through dashboards and logs to piece together what happened.
Fortunately, unsupervised machine learning can be applied to logs and metrics to automatically find the root cause of software incidents. And it can be done without manual training or large training datasets. Read this whitepaper to learn about the technologies and approaches, with a deep dive into Zebrium's implementation, for automatically finding the root cause using logs and matrics.