  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
consider a comprehensive set of non-functional criteria, such as the
integrability with ML pipelines, the adaptability to diverse data types,
user-friendliness, computational efficiency, and resource demands. Our findings
reveal that Evidently AI stands out for its general data drift detection,
whereas NannyML excels at pinpointing the precise timing of shifts and
evaluating their consequent effects on predictive accuracy.
