Network monitoring has been evolving over several years to be able to identify and react to issues at a faster rate to reduce network downtime. With the expansion of cloud-users and the need for higher networking capability, the deployments are vast and complex, constantly making network troubleshooting slower. In this paper, we introduce a novel network monitoring and troubleshooting architecture that is user-defined, allowing users to define their troubleshooting profiles for a network device. We introduce the framework that can be easily integrated with the network device operating system which can work either independently on the device or can be aggregated across multiple devices in a network deployment. We present use-cases where this device-level telemetry abstraction would be very useful and how it can be easily extended. Also, we describe the machine-learning aspect which predicts thresholds based on data patterns, thus automating responses when an anomalous event occurs. Finally, we analyze the footprint the framework would have on a real network device and its overall benefit for the network in terms of latency and reaction time.