Your team recently deployed a Deep Neural Network (DNN) regression model, which had shown good performance during training and testing phases. However, six months post-deployment, you observe a significant drop in the model's performance. This decline is attributed to a change in the distribution of the input data, indicating data drift. To address the differences in input data distribution and restore the model's performance in production, what steps should you take?