Searched for: subject%3A%22Drift%255C+Detection%22
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document
André, Baptiste (author)
When deployed in production, machine learning models sometimes lose accuracy over time due to a change in the distribution of the incoming data, which results in the model not reflecting reality any longer. A concept drift is this loss of accuracy over time. Drift detectors are algorithms used to detect such drifts. Drift detectors are important...
bachelor thesis 2023
document
Poenaru-Olaru, L. (author), Cruz, Luis (author), Rellermeyer, Jan S. (author), van Deursen, A. (author)
AIOps solutions enable faster discovery of failures in operational large-scale systems through machine learning models trained on operation data. These models become outdated during the occurrence of concept drift, a term used to describe shifts in data distributions. In operation data concept drift is inevitable and it impacts the...
conference paper 2023
document
Poenaru-Olaru, L. (author), Cruz, Luis (author), van Deursen, A. (author), Rellermeyer, Jan S. (author)
As machine learning models increasingly replace traditional business logic in the production system, their lifecycle management is becoming a significant concern. Once deployed into production, the machine learning models are constantly evaluated on new streaming data. Given the continuous data flow, shifting data, also known as concept drift,...
conference paper 2022
document
Poenaru-Olaru, L. (author)
Machine learning systems both gained significant interest from the academic side and have seen adoption in the industry. However, one aspect that has received insufficient attention so far is the study of the lifecycle of such systems. This aspect is particularly important due to various ML systems' strong dependency on data, which is...
conference paper 2021
Searched for: subject%3A%22Drift%255C+Detection%22
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