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Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability. In response to this challenge, we present a microbenchmark study, called D3Bench, which evaluates the efficacy of ope
Externí odkaz:
http://arxiv.org/abs/2404.18673
