Model Drift
Model drift occurs when an AI model's performance declines over time because real-world data begins to differ from the data it was originally trained on. As patterns shift, the model becomes less accurate, making ongoing monitoring and retraining essential to maintain reliability.
Why it Matters:
Continuous monitoring and retraining are essential to maintain model reliability and prevent business risk.
In custom software development projects, QAT Global can implement monitoring pipelines to detect and correct model drift. Our IT Staffing teams help by sourcing data engineers and MLOps specialists to maintain ongoing model health and performance.
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