There are several reasons why you might want to track analytics from operational predictions made by a text classification model. First, tracking analytics can provide valuable insights into the performance of the model. This can include metrics such as the accuracy of the model’s predictions, the speed at which it processes data, and the number of predictions it makes over a given period of time. These metrics can help you understand how well the model is performing and identify areas for improvement.
Second, tracking analytics can also help you monitor the impact of the model on your business or organization. For example, if the model is being used to classify customer feedback, tracking analytics can help you understand how the model’s predictions are affecting customer satisfaction and loyalty. This can help you make more informed decisions about how to use the model and optimize its performance.
Third, tracking analytics can also provide valuable data for testing and evaluating the model. For example, you could use analytics data to compare the performance of different versions of the model or to evaluate the effect of different parameters on its accuracy. This can help you make more informed decisions about how to develop and improve the model over time.