Detecting anomalies from satellite telemetry is critical for the safe operation of that satellite. Although many approaches to autonomous onboard anomaly detection have already been proposed, most of them have so far only been tested on non-satellite or simulated data. This activity tackled this research gap and proposed an end-to-end machine learning-powered approach for detecting abnormal events in real-life OPS-SAT telemetry data, and deployed it on board a satellite. The experimental study revealed that our technique achieves the classification accuracy of 0.957 over the unseen and validated test set, with precision and recall of 0.929 and 0.897, respectively, while offering very fast inference.