Model Drift
Model drift occurs when an AI model's performance degrades over time because the real-world data it processes changes significantly from its training data.
AI models are trained on specific datasets, but the real world is constantly changing. Customer preferences shift, market conditions evolve, and new trends emerge. When the data an AI model encounters in live operation becomes too different from the data it was originally trained on, its accuracy and effectiveness can decline – this is called model drift.
For a small business, model drift can lead to AI tools making incorrect predictions or recommendations, costing money, or reducing efficiency. Regular monitoring and retraining of AI models are necessary to combat model drift and keep your AI performing optimally.
An AI-powered inventory management system might experience model drift if customer purchasing habits dramatically shift after a new product launch, leading to incorrect stock predictions and potential overstocking or shortages.