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Model validation comprises practices and criteria to evaluate machine learning models, ensuring robustness, generalization and fairness. It defines tests, metrics and acceptance criteria across training and production stages.
Model validation describes practices for evaluating and assuring machine learning models using tests, metrics and data checks. The goal is to ensure robustness, generalization and fairness and to detect data issues or unintended behavior early. It focuses on reproducible validation pipelines and documented acceptance criteria across training, validation and production stages.
Key indicator of model quality on validation data.
Measure of change between training and production data.
Assessment of disparities in model decisions across groups.
Regular score tests, backtests against historical data and fairness checks before every release.
Monitor production metrics of user interactions; on drift an automated validation workflow and retraining run.
TensorFlow Data Validation to detect schema deviations and data anomalies before model training.
Define clear acceptance criteria and metrics.
Automate data and model checks in the CI/CD pipeline.
Integrate drift and performance monitoring for production.
Create reproducible validation artifacts and reports.
Conduct regular audits and fairness reviews.