Open-Source Drift Detection Tools in Action: Insights from Two Use Cases
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866914789812338688 |
|---|---|
| author | Müller, Rieke Abdelaal, Mohamed Stjelja, Davor |
| author_facet | Müller, Rieke Abdelaal, Mohamed Stjelja, Davor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18673 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Open-Source Drift Detection Tools in Action: Insights from Two Use Cases Müller, Rieke Abdelaal, Mohamed Stjelja, Davor Databases Machine Learning 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. |
| title | Open-Source Drift Detection Tools in Action: Insights from Two Use Cases |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2404.18673 |