Monitoring and explainability of models in production

Monitoring and explainability of models in production

13 July 2020

Papers citing "Monitoring and explainability of models in production"

14 / 14 papers shown

Title
Estimating Model Performance Under Covariate Shift Without Labels Jakub Bialek W. Kuberski Nikolaos Perrakis Albert Bifet 39 2 0 16 Jan 2024
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens Jean Feng Adarsh Subbaswamy Alexej Gossmann Harvineet Singh B. Sahiner Mi-Ok Kim Gene Pennello N. Petrick Romain Pirracchio Fan Xia 19 1 0 20 Nov 2023
Monitoring Machine Learning Models: Online Detection of Relevant Deviations Florian Heinrichs 30 2 0 26 Sep 2023
DEPLOYR: A technical framework for deploying custom real-time machine learning models into the electronic medical record Conor K. Corbin R. Maclay Aakash Acharya Sreedevi Mony Soumya Punnathanam Rahul Thapa N. Kotecha N. Shah Jonathan H. Chen 15 17 0 11 Mar 2023
A monitoring framework for deployed machine learning models with supply chain examples Bradley Eck Duygu Kabakci-Zorlu Yan Chen France Savard Xiao-Hua Bao 17 4 0 11 Nov 2022
Desiderata for next generation of ML model serving Sherif Akoush Andrei Paleyes A. V. Looveren Clive Cox 33 5 0 26 Oct 2022
Computer vision based vehicle tracking as a complementary and scalable approach to RFID tagging P. Gaur Abhilasha Bhardwaj P. Shete Mohini Laghate D. Sarode 18 0 0 13 Sep 2022
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a Pedestrian Automatic Emergency Brake System Markus Borg Jens Henriksson Kasper Socha Olof Lennartsson Elias Sonnsjo Lonegren T. Bui Piotr Tomaszewski S. Sathyamoorthy Sebastian Brink M. H. Moghadam 35 23 0 16 Apr 2022
CheXstray: Real-time Multi-Modal Data Concordance for Drift Detection in Medical Imaging AI Arjun Soin J. Merkow Jin Long Joseph Paul Cohen Smitha Saligrama Stephen Kaiser Steven Borg Ivan Tarapov M. Lungren OOD 53 15 0 06 Feb 2022
MLOps -- Definitions, Tools and Challenges Georgios Symeonidis Evangelos Nerantzis A. Kazakis G. Papakostas 32 90 0 01 Jan 2022
Ensembling Shift Detectors: an Extensive Empirical Evaluation Simona Maggio L. Dreyfus-Schmidt AI4TS 34 3 0 28 Jun 2021
Pitfalls of Explainable ML: An Industry Perspective Sahil Verma Aditya Lahiri John P. Dickerson Su-In Lee XAI 16 9 0 14 Jun 2021
Drug discovery with explainable artificial intelligence José Jiménez-Luna F. Grisoni G. Schneider 30 625 0 01 Jul 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,675 0 05 Dec 2016