# McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions

arXiv: https://arxiv.org/abs/2401.14093 · HTML: https://mlsanity.com/paper/2401.14093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 11 of 11)

- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors](https://mlsanity.com/paper/2203.11070/) (2022)
- [Software Engineering Challenges of Deep Learning](https://mlsanity.com/paper/1810.12034/) (2018)
- [AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech](https://mlsanity.com/paper/2010.02716/) (2020)
- [Unbiased Measurement of Feature Importance in Tree-Based Methods](https://mlsanity.com/paper/1903.05179/) (2019)
- [A Debiased MDI Feature Importance Measure for Random Forests](https://mlsanity.com/paper/1906.10845/) (2019)
- [The impact of feature importance methods on the interpretation of defect classifiers](https://mlsanity.com/paper/2202.02389/) (2022)
- [Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion](https://mlsanity.com/paper/2009.05501/) (2020)
- [Towards a consistent interpretation of AIOps models](https://mlsanity.com/paper/2202.02298/) (2022)
- [Are Concept Drift Detectors Reliable Alarming Systems? -- A Comparative Study](https://mlsanity.com/paper/2211.13098/) (2022)
- [On the Model Update Strategies for Supervised Learning in AIOps Solutions](https://mlsanity.com/paper/2311.03213/) (2023)

## Built on by (most cited first, 1 of 1)

- [Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation](https://mlsanity.com/paper/2506.17442/) (2025)

## Abstract (arXiv)

Due to the continuous change in operational data, AIOps solutions suffer from performance degradation over time. Although periodic retraining is the state-of-the-art technique to preserve the failure prediction AIOps models' performance over time, this technique requires a considerable amount of labeled data to retrain. In AIOps obtaining label data is expensive since it requires the availability of domain experts to intensively annotate it. In this paper, we present McUDI, a model-centric unsupervised degradation indicator that is capable of detecting the exact moment the AIOps model requires retraining as a result of changes in data. We further show how employing McUDI in the maintenance pipeline of AIOps solutions can reduce the number of samples that require annotations with 30k for job failure prediction and 260k for disk failure prediction while achieving similar performance with periodic retraining.