Model drift refers to the degradation of model performance due to changes in data and relationships between input and output variables. It is relatively common for model drift to impact an organization negatively over time or sometimes suddenly. To effectively detect and mitigate drift, organizations can monitor and manage model performance as part of data and AI platform. This integrated approach to data and AI can help you:
- Track metrics continually and get alerted on drift in accuracy and data consistency.
- Set targets and track them through development, validation and deployment.
- Simplify steps to identify business metrics affected by model drift.
- Minimize the impact of model degradation through automating drift monitoring.
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