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Monitoring Machine Learning Systems: A Multivocal Literature Review
Hira Naveed, Scott Barnett, Chetan Arora +3
Context: Dynamic production environments make it challenging to maintain reliable machine learning (ML) systems. Runtime issues, such as changes in data patterns or operating conte…
Understanding Practitioners Perspectives on Monitoring Machine Learning Systems
Hira Naveed, John Grundy, Chetan Arora +2
Given the inherent non-deterministic nature of machine learning (ML) systems, their behavior in production environments can lead to unforeseen and potentially dangerous outcomes. F…
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review
Hira Naveed, Chetan Arora, Hourieh Khalajzadeh +2
Context: Machine Learning (ML) has become widely adopted as a component in many modern software applications. Due to the large volumes of data available, organizations want to incr…
Runtime Monitoring of Human-centric Requirements in Machine Learning Components: A Model-driven Engineering Approach
Hira Naveed
As machine learning (ML) components become increasingly integrated into software systems, the emphasis on the ethical or responsible aspects of their use has grown significantly. T…