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cs.SE2026
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…
cs.SE2026
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…