Concept and Data Drift in Deep Learning

Key TakeawaysModels assume constant patterns, but real-world data changes; monitor for concept drift (shift in relationships) for accurate predictions.Data characteristics evolve over time; be vigilant for data drift (distributional changes) to maintain model relevance.Utilize monitoring tools to detect shifts in data patterns, ensuring models adapt to changing environments.Regularly update models with new data to counter concept and data drift, preserving prediction accuracy. Continuous...