11 citations · 16 across the 8 of their papers we have counts for
4 papers · 1 filter
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…