After a model has been deployed, it needs to be maintained. The data being fed may change over time. For example, in the case of a model predicting house prices, the prices of houses may rise over time or fluctuate due to some other factor. The accuracy of the model on new data can be recorded. Some common ways to ensure accuracy include-
The model should be frequently checked by feeding negative test data. If the model gives low accuracy with negative test data, it is fine.
An Auto Encoder should be built that Using anomaly detection techniques, the AE model will calculate the Reconstruction error value. If the Reconstruction error value is high, it means the new data does not follow the old pattern learned by the model.
If the model shows good prediction accuracy with new data, it means that the new data follows the pattern or the generalization learned by the model on old data. So, the model can be retrained on the new data. If the accuracy on new data is not that good, the model can be retrained on the new data with feature engineering on the data features along with the old data.
If the accuracy is not good, the model may need to be trained from scratch.