11 citations · 16 across the 8 of their papers we have counts for
8 papers
Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review
Faezeh Amou Najafabad, Markus Haug, Keerthiga Rajenthiram +2
Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack of consolidated architectural…
A Systematic Review of MLOps Tools: Tool Adoption, Lifecycle Coverage, and Critical Insights
Zakkarija Micallef, Keerthiga Rajenthiram, Ilias Gerostathopoulos
Machine Learning Operations (MLOps) has become increasingly critical as more organisations move ML models into production. However, the growing landscape of MLOps solutions has int…
Monitoring and Observability of Machine Learning Systems: Current Practices and Gaps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Production machine learning (ML) systems fail silently -- not with crashes, but through wrong decisions. While observability is recognized as critical for ML operations, there is a…
Tracing Distribution Shifts with Causal System Maps
Joran Leest, Ilias Gerostathopoulos, Patricia Lago +1
Monitoring machine learning (ML) systems is hard, with standard practice focusing on detecting distribution shifts rather than their causes. Root-cause analysis often relies on man…
From Tea Leaves to System Maps: A Survey and Framework on Context-aware Machine Learning Monitoring
Joran Leest, Claudia Raibulet, Patricia Lago +1
Machine learning (ML) models in production fail when their broader systems -- from data pipelines to deployment environments -- deviate from training assumptions, not merely due to…
How Do Model Export Formats Impact the Development of ML-Enabled Systems? A Case Study on Model Integration
Shreyas Kumar Parida, Ilias Gerostathopoulos, Justus Bogner
Machine learning (ML) models are often integrated into ML-enabled systems to provide software functionality that would otherwise be impossible. This integration requires the select…
An Analysis of MLOps Architectures: A Systematic Mapping Study
Faezeh Amou Najafabadi, Justus Bogner, Ilias Gerostathopoulos +1
Context. Despite the increasing adoption of Machine Learning Operations (MLOps), teams still encounter challenges in effectively applying this paradigm to their specific projects.…
Expert-Driven Monitoring of Operational ML Models
Joran Leest, Claudia Raibulet, Ilias Gerostathopoulos +1
We propose Expert Monitoring, an approach that leverages domain expertise to enhance the detection and mitigation of concept drift in machine learning (ML) models. Our approach sup…