Which of the following is a challenge faced during model deployment?

Managing changes in data input over time is indeed a significant challenge during model deployment. When a machine learning model is deployed in a real-world environment, it may encounter data that differs from what it was originally trained on, often referred to as "data drift" or "concept drift." This can occur due to various factors, such as changes in user behavior, market dynamics, or the introduction of new products or services.

Addressing these changes is crucial, as the performance of the model can degrade if it is not able to adapt to new patterns in the data. This may require ongoing monitoring of the model's performance and potentially retraining it with new data to ensure it remains accurate and reliable.

In contrast, verifying model accuracy on historical data, while important for assessing initial performance, does not address the challenges that arise once the model is operating in the field. Similarly, generating more training data and automating feature engineering are tasks that are typically part of the model development process rather than aspects of deployment itself. Thus, the focus on managing data input changes highlights the dynamic nature of the deployment environment and the need for adaptability in deployed models.