Towards a low-code solution for monitoring machine learning model performance

Author:

Kourouklidis Panagiotis1,Kolovos Dimitris1,Matragkas Nicholas1,Noppen Joost2

Affiliation:

1. University of York

2. British Telecom

Funder

H2020 Marie Sk?odowska-Curie Actions

Publisher

ACM

Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Runtime Monitoring of Human-Centric Requirements in Machine Learning Components: A Model-Driven Engineering Approach;2023 ACM/IEEE International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C);2023-10-01

2. Leveraging Feature Bias for Scalable Misprediction Explanation of Machine Learning Models;2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE);2023-05

3. Optimization for achieving sustainability in low code development platform;International Journal on Interactive Design and Manufacturing (IJIDeM);2023-04-21

4. NCARVis: No-Code Visualization Creation System based on Free-hand;2023 IEEE 16th Pacific Visualization Symposium (PacificVis);2023-04

5. A Study of Mental Health Self-Monitoring Based on the Combination of BERT and Low-Code Platform;2023 6th World Conference on Computing and Communication Technologies (WCCCT);2023-01-06

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