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Overview
What is model drift? Why does it matter?
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.
Learn more
Visualize model drift
Continuously monitor models. Alert users on drift in model accuracy and data consistency.
Dig deeper
Keep AI models accurate
Get under the hood to learn how the drift monitor works.
Track AI models
Experience a hands-on tour of model evaluation and management.
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Detecting drift
Monitor any drift in model accuracy and receive an alert when accuracy degrades past a threshold.

Resources
Get started with explainable AI
Explore model monitoring and model management in IBM Watson Studio.