Thorough Testing and Monitoring Vital for AI System Reliability
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Test feature engineering and data processing thoroughly, not just model predictions. Use approximate asserts for predictions.
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Integration testing across the entire pipeline catches issues from hand-offs between components.
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Validate inputs early and fail fast if outside expected ranges to avoid unpredictable behavior.
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Execute on as much real data as possible before deployment to catch edge cases. Consider a "soft launch" to small user group.
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Monitor for prediction, feature, and concept drift once deployed to production to detect when retraining is needed.