Operations | Monitoring | ITSM | DevOps | Cloud

When troubleshooting an issue with logs, it’s often fairly easy to find an important log; for example, an exception is logged and an alert is fired. Once you find an exception in the logs, the trickier part can be understanding why the exception was thrown. One common approach to this problem is looking at the surrounding logs. Logs are typically in chronological order, so if you see an exception, look at the logs’ lines from just before the exception occurred.

Today we announced an integration between SolarWinds® AppOptics™ and SolarWinds Papertrail™ to allow you to quickly move from service-level metrics, down to a trace, and then down to the logs specific to that trace. The integration between AppOptics and Papertrail provides the ability to group the log messages from a traced transaction and add trace context to your logs in Papertrail.

Many data science discussions focus on model development. But as any data scientist will tell you, this is only a small—and often relatively quick—part of the data science pipeline. An important, but often overlooked, component of model stewardship is monitoring models once they’ve been released to the wild. Here we’ll aim to convince any unbelievers that monitoring deployed models is as important as any other task in the data science workflow.