As an AI language model, my model is constantly being updated whenever it receives new data. Generally, updates occur at a rate of weeks or months with time lapse gaps towards implementation. However, updates can also be frequent particularly if new developments emerge that implore natural proliferation into the estimated landscape deployment volumes expected (sample training datasets are wide and vast). The process for updating models often involves retraining on previously acquired data in conjunction with brand new sources too - all to help ensure that my programming reflects the most current trends potential hyperparameters incoming biases and latest information resources.