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Found 6 papers, 2 papers with code
McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions
Due to the continuous change in operational data, AIOps solutions suffer from performance degradation over time.
Data vs. Model Machine Learning Fairness Testing: An Empirical Study
We find a linear relationship between data and model fairness metrics when the distribution and the size of the training data changes.
Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World
Anomaly detection techniques are essential in automating the monitoring of IT systems and operations.
Green Runner: A tool for efficient model selection from model repositories
Deep learning models have become essential in software engineering, enabling intelligent features like image captioning and document generation.
Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study
We compare the performance of the most popular drift detectors belonging to two different concept drift detector groups, error rate-based detectors and data distribution-based detectors.
Data Smells in Public Datasets
The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI.
