Operations | Monitoring | ITSM | DevOps | Cloud

Running or operating applications requires several tasks throughout their lifecycle: scaling instances, checking the health, integrating with other applications, running backups, and applying updates – to name a few examples. It’s a time and labour-intensive process. To automate these tasks, developers can implement scripts for repeated execution. This is where the software operator comes in.

It’s monitoring time. We all collect metrics from our system and applications to monitor their health, availability and performance. Our metrics are essentially time-series data collected from various endpoints. Then, it is stored in time series specialized databases, and then visualized in the metrics graphs we all know and love.

Following on from the recent launch of our Anomaly Advisor feature, and in keeping with our approach to machine learning, here is a detailed Python notebook outlining exactly how the machine learning powering the Anomaly Advisor actually works under the hood. Or if you’d rather watch a video walkthrough of the notebook then check out below. Try it for yourself, get started by signing in to Netdata and connecting a node.