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Scanflow: A multi-graph framework for Machine Learning workflow management, supervision, and debugging
[article]
2021
arXiv
pre-print
Machine Learning (ML) is more than just training models, the whole workflow must be considered. Once deployed, a ML model needs to be watched and constantly supervised and debugged to guarantee its validity and robustness in unexpected situations. Debugging in ML aims to identify (and address) the model weaknesses in not trivial contexts. Several techniques have been proposed to identify different types of model weaknesses, such as bias in classification, model decay, adversarial attacks, etc.,
arXiv:2111.03003v1
fatcat:lpllikyryff4zmyridyuuc4vtu