Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
This addresses data drift challenges for ML practitioners, but it is incremental as it benchmarks existing tools without introducing new methods.
The study tackled the problem of data drift in ML models by evaluating open-source drift detection tools (Evidently AI, NannyML, Alibi-Detect) using real-world smart building data, finding that Evidently AI excels in general drift detection while NannyML is better at timing shifts and assessing accuracy impacts.
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.