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<\/figure>\n\n\n\nOne of the things you can do at this point is retraining the model; and here again feedback API is your friend. In fact, combining the readings from meteorological sensors with the data coming from the feedback API you always have a fresh stream of ground truth data that can be used for retraining at any time.<\/p>\n\n\n\n
Of course a weather prediction model is just an example of the problem and its solution. The same applies to any kind of predictive model. Take for instance a Computer Vision model used for Object Detection: this too is a form of prediction and therefore subject to the same risk of degrading results. Luckily for you, it is also open to the help of the feedback API.<\/p>\n\n\n\n
The solution will be more complex and may require, for example, data annotation tools to check and annotate the truthfulness of the predictions. But the basic concept of comparing predictions and ground truth coming (at a later stage) from feedback API, and computing metrics to check model performance \u2013 that basic concept remains the same.<\/p>\n\n\n\n