Why do TensorFlow models require retraining in production systems?

Models require retraining due to evolving data distributions and concept drift.

Production environments are dynamic. User behavior, market trends, and system inputs evolve. Over time, the model's learned patterns become outdated. This causes performance degradation. Continuous retraining ensures adaptation to new distributions.