We propose an approach following the serverless computing model for ML systems, incorporating covariate drift detection methods that process data in batch mode ...
Efficient and scalable covariate drift detection in machine learning systems with serverless computing. https://doi.org/10.1016/j.future.2024.07.010 ·.
This paper addresses the gap in the widespread adoption of drift detection techniques by proposing a serverless-based approach for batch covariate drift ...
Jul 17, 2024 · Our latest paper, "Efficient and scalable covariate drift detection in machine learning systems with serverless computing" features our ...
This repository contains the code to reproduce the training and deployment of the proposed serverless architecture for data drift detection of the paper ...
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Efficient and scalable covariate drift detection in machine learning systems with serverless computing. Future Gener. Comput. Syst. 161: 174-188 (2024). [j1].
In this work we present a machine learning based approach for detecting drifting behavior – so-called concept drifts – in continuous data streams.
Efficient and scalable covariate drift detection in machine learning systems with serverless computing. Zenodo. https://doi.org/10.1016/j.future.2024.07.010.
Efficient and scalable covariate drift detection in machine learning systems with serverless computing ... training of machine learning and deep learning ...
machine-learning-systems.pdf.jpg, dic-2024, Efficient and scalable covariate drift detection in machine learning systems with serverless computing · Céspedes ...