Model Drift

The degradation in performance of an AI model over time due to changes in input data or external conditions.

This degradation occurs when new data no longer resembles the data used during training, reducing the model’s accuracy and relevance.

ModelOp addresses model drift by enabling continuous monitoring of deployed models through its ModelOp Center platform. From a data science perspective, drift signals that inputs have diverged from training conditions. Operational teams monitor resource use, latency, and throughput, while business teams assess cost efficiency and SLA adherence. Detecting and correcting drift is essential for maintaining trust, performance, and business value in AI systems.

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