A Metrics-Oriented Architectural Model to Characterize Complexity on Machine Learning-Enabled Systems
This work addresses the challenge of complexity management for developers and architects of ML-enabled systems, but it appears incremental as it builds on existing reference architectures.
The paper tackles the problem of managing complexity in machine learning-enabled systems by introducing a metrics-based architectural model to characterize and support architectural decisions, with the initial step being an extension of a reference architecture to collect metrics.
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.