A research proposal describing a planned metrics-based model for ML-enabled system complexity, illustrated with two architecture diagrams, but containing no computed metrics or validated results.
A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
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abstract
How can the complexity of ML-enabled systems be managed effectively? The goal of this research is to investigate how complexity affects ML-Enabled Systems (MLES). To address this question, this research aims to introduce a metrics-based architectural model to characterize the complexity of MLES. The goal is to support architectural decisions, providing a guideline for the inception and growth of these systems. This paper showcases the first step for creating the metrics-based architectural model: an extension of a reference architecture that can describe MLES to collect their metrics.
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cs.SE 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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A Tale of Two Systems: Characterizing Architectural Complexity on Machine Learning-Enabled Systems
A research proposal describing a planned metrics-based model for ML-enabled system complexity, illustrated with two architecture diagrams, but containing no computed metrics or validated results.