What is a primary risk of not implementing automatic retraining of models?

A primary risk of not implementing automatic retraining of models is stagnant model performance over time. As time progresses, the underlying data distributions may shift due to various factors such as changes in user behavior, market conditions, or other external influences. This phenomenon is often referred to as concept drift. If a model is not regularly retrained with up-to-date data, its performance can degrade significantly because it continues to operate based on outdated information. Consequently, predictions may become less accurate, potentially leading to poor decision-making and a negative impact on business outcomes.

In contrast, other options such as computational efficiency, costs due to resource allocation, and data security, while certainly relevant concerns in the broader context of model management and deployment, are not directly tied to the risk of performance stagnation. Automatic retraining primarily addresses the need for maintaining and improving the accuracy and relevance of the model over time in the face of changing data characteristics.