mlsanityData to 7 Oct 2026

Monitoring and explainability of models in production

arXiv 2007.06299 ↗First posted 13 Jul 2020stat.ML cs.LGcited by 23

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Abstract (arXiv)

The machine learning lifecycle extends beyond the deployment stage. Monitoring deployed models is crucial for continued provision of high quality machine learning enabled services. Key areas include model performance and data monitoring, detecting outliers and data drift using statistical techniques, and providing explanations of historic predictions. We discuss the challenges to successful implementation of solutions in each of these areas with some recent examples of production ready solutions using open source tools.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2020.

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Citing papers per year: 2021: 3; 2022: 7; 2023: 5; 2024: 4; 2025: 2; 2026: 2.

23 papers.

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  1. 2022MLOps -- Definitions, Tools and Challengescited by 25
  2. 2022Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake Systemcited by 14
  3. 2021ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systemscited by 9
  4. 2022Context-Aware Drift Detectioncited by 9
  5. 2024Estimating Model Performance Under Covariate Shift Without Labelscited by 9
  6. 2024Confidence-based Estimators for Predictive Performance in Model Monitoringcited by 8
  7. 2022CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AIcited by 7
  8. 2022Desiderata for next generation of ML model servingcited by 5
  9. 2022A monitoring framework for deployed machine learning models with supply chain examplescited by 5
  10. 2023DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical recordcited by 5
  11. 2023Explanation Shift: How Did the Distribution Shift Impact the Model?cited by 5
  12. 2021Pitfalls of Explainable ML: An Industry Perspectivecited by 3
11 more
  1. 2023Monitoring Machine Learning Models: Online Detection of Relevant Deviationscited by 3
  2. 2023Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lenscited by 3
  3. 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 3
  4. 2021Ensembling Shift Detectors: an Extensive Empirical Evaluationcited by 2
  5. 2023Causal fault localisation in dataflow systemscited by 1
  6. 2024Machine Learning Operations: A Mapping Studycited by 1
  7. 2022Computer vision based vehicle tracking as a complementary and scalable approach to RFID taggingcited by 0
  8. 2025Perspectives on a Reliability Monitoring Framework for Agentic AI Systemscited by 0
  9. 2025Latent space analysis and generalization to out-of-distribution datacited by 0
  10. 2026ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Valuescited by 0
  11. 2026Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generationcited by 0