Unverified50% confidenceFactExact time
Data drift can usually be detected by monitoring statistical distances between the input distribution and the training distribution (such as PSI or KL divergence), whereas concept drift can only be caught through labeled online evaluation.
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50%
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Long-term
Relevance
10/25/2026
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UnverifiedA common practice in observation normalization is to use a running mean/std to normalize observations online, but care must be taken to freeze the statistics to the inference side after training ends, otherwise distribution shift will occur during deployment.78% similarUnverifiedBy projecting input activations onto a small subset of the principal components of the training distribution, probes can achieve cross-domain transfer with performance nearly matching that of probes trained directly on the test distribution.77% similarUnverifiedThe industry typically monitors the degree of data drift continuously using statistical metrics such as PSI (Population Stability Index) and KL divergence, triggering model retraining when thresholds are exceeded.75% similarUnverifiedCoping with feature drift typically requires continuously monitoring feature distributions, setting alert thresholds, retraining periodically or on trigger, and keeping version snapshots of training data and feature definitions.74% similarUnverified批次归一化在训练模式使用当前批次统计量，在推理模式使用训练过程中累积的移动平均值71% similar
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