In a recent project for the European Space Agency, Armin Müller from ScopeSET explored ways to expedite the training of machine learning models by integrating Grafana into their workflow. The project involved analyzing time series data from satellite sensors to support automated anomaly detection, and the data was initially stored in a PostgreSQL database as part of the CubeSAT MOVE-II demo mission. By transitioning from slower CSV visualization tools to Grafana, the team significantly reduced the time needed to process and visualize large datasets, enhancing their ability to identify nominal data ranges for model training. This integration allowed for a more efficient analysis process, as Grafana's performance and UI responsiveness excelled with over 100,000 rows of data, compared to previous tools. The use of Grafana, combined with JupyterLab and Python, streamlined the anomaly detection workflow, offering a multiplier effect in efficiency across various machine learning models due to its capacity to handle diverse anomaly scenarios.