How often you should retrain your AI models depends largely on the nature of your data, the stability of your environment, and how critical accuracy is to your business. At AEHEA, we view retraining not as a fixed schedule, but as a response to changes in data patterns, user behavior, or performance metrics. Regular retraining ensures your AI remains effective, accurate, and aligned with your current business realities.

In stable environments where data changes slowly, retraining might be needed only once or twice a year. If your AI is used in predictable scenarios with limited variability, infrequent retraining can be sufficient. However, in more dynamic situations like eCommerce, finance, or marketing where user behaviors and external conditions can change quickly, retraining monthly or even weekly can be essential. The key factor is how quickly the underlying data or business context changes.

We recommend implementing a monitoring system that tracks model performance in real-time. By measuring accuracy, consistency, and user feedback, you can identify precisely when performance starts to degrade. This performance driven approach means retraining happens exactly when needed, rather than relying on guesswork. Tools like MLflow or Weights and Biases can automate this monitoring and trigger retraining automatically based on predefined thresholds.

At AEHEA, we typically combine regular scheduled retraining with event-driven updates triggered by performance drops or detected model drift. This hybrid approach offers both stability and flexibility. Regularly reviewing performance data and user feedback allows us to adjust the retraining strategy as necessary. Keeping models current and accurate is an ongoing task, but when handled systematically, it ensures your AI investment continues delivering measurable value.