In the world of predictions we try to summarize how well we do with some over-arching metrics such as P-values, F-scores, or precision and recall.

While a useful shorthand, these metrics are abstract and don't provide all relevant details (how could they). So as an alternative, it's nice to be able to pull-out a time-series database (Influxdb) and visualization platform (Grafana) and plot out our predictions followed by the actual results. Sometimes a simple graph can be worth a thousand KPIs.



The above is a trivial example where we pull the weather forecast from a public API, and later the actual weather from sensors managed by our friends at Alvasys. The red and blue values are predictions, and the green values are actuals.

Now imagine that instead of weather data, you can visualize your ML/AI predictions alongside the actual results. Influxdb and Grafana make it easy to query by time, model type or any dynamic metric you add as metadata. These graphs can be a precious complement to your over-arching metrics and help you form a more holistic understanding of your model performance.