{"total":23,"rows":[{"id":"2201.00162","title":"MLOps -- Definitions, Tools and Challenges","created":"2022-01-01","cited_by_count":25,"area":null},{"id":"2204.07874","title":"Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System","created":"2022-04-16","cited_by_count":14,"area":null},{"id":"2106.03122","title":"ModelCI-e: Enabling Continual Learning in Deep Learning Serving Systems","created":"2021-06-06","cited_by_count":9,"area":null},{"id":"2203.08644","title":"Context-Aware Drift Detection","created":"2022-03-16","cited_by_count":9,"area":null},{"id":"2401.08348","title":"Estimating Model Performance Under Covariate Shift Without Labels","created":"2024-01-16","cited_by_count":9,"area":null},{"id":"2407.08649","title":"Confidence-based Estimators for Predictive Performance in Model Monitoring","created":"2024-07-11","cited_by_count":8,"area":null},{"id":"2202.02833","title":"CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI","created":"2022-02-06","cited_by_count":7,"area":null},{"id":"2303.08081","title":"Explanation Shift: How Did the Distribution Shift Impact the Model?","created":"2023-03-14","cited_by_count":6,"area":null},{"id":"2210.14665","title":"Desiderata for next generation of ML model serving","created":"2022-10-26","cited_by_count":5,"area":null},{"id":"2211.06239","title":"A monitoring framework for deployed machine learning models with supply chain examples","created":"2022-11-11","cited_by_count":5,"area":null},{"id":"2303.06269","title":"DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record","created":"2023-03-11","cited_by_count":5,"area":null},{"id":"2106.07758","title":"Pitfalls of Explainable ML: An Industry Perspective","created":"2021-06-14","cited_by_count":4,"area":null},{"id":"2410.21346","title":"Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approach","created":"2024-10-28","cited_by_count":4,"area":null},{"id":"2309.15187","title":"Monitoring Machine Learning Models: Online Detection of Relevant Deviations","created":"2023-09-26","cited_by_count":3,"area":null},{"id":"2311.11463","title":"Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens","created":"2023-11-20","cited_by_count":3,"area":null},{"id":"2106.14608","title":"Ensembling Shift Detectors: an Extensive Empirical Evaluation","created":"2021-06-28","cited_by_count":2,"area":null},{"id":"2304.11987","title":"Causal fault localisation in dataflow systems","created":"2023-04-24","cited_by_count":1,"area":null},{"id":"2409.19416","title":"Machine Learning Operations: A Mapping Study","created":"2024-09-28","cited_by_count":1,"area":null},{"id":"2209.05911","title":"Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging","created":"2022-09-13","cited_by_count":0,"area":null},{"id":"2511.15010","title":"Latent space analysis and generalization to out-of-distribution data","created":"2025-11-19","cited_by_count":0,"area":null},{"id":"2604.11200","title":"ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values","created":"2026-04-13","cited_by_count":0,"area":null},{"id":"2608.27076","title":"Tabular Deep Learning for Algorithmic Trading: Cross-Regime Bayesian Optimisation for Equity Signal Generation","created":"2026-08-27","cited_by_count":0,"area":null},{"id":"2610.10957","title":"TRACE: A Governance Framework for Measuring Explainability Debt in Production AI Systems","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}