Open-Source Drift Detection Tools in Action: Insights from Two Use Cases

arXiv:2404.18673v1 Announce Type: cross
Abstract: 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 open-source drift detection tools. D3Bench examines the capabilities of Evidently AI, NannyML, and Alibi-Detect, leveraging real-world data from two smart building use cases.We prioritize assessing the functional suitability of these tools to identify and analyze data drifts. Furthermore, we …

abstract arxiv capabilities cases challenge cs.db cs.lg data detection detection tools drift insights lifecycle machine machine learning performance reliability study tools type use cases