Data Drift Assessment

From The Foundation for Best Practices in Machine Learning

Data Drift Assessment


Document and assess historic and prospective changes in data distribution, inclusive of missing and nonsensical data. If data drift is apparent and/or expected in the future, implement mitigating measures as much as is reasonably practical.


To (a) assess and promote the stability of data distributions (data drift); (b) determine the need for data distributions monitoring, risk-based mitigation strategies and responses, drift resistance and adaptation simulations and optimization, and data distribution calibration; and (c) highlight associated risks that might occur in the Product Lifecycle.

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