Multi-View Modeling Framework for ML Systems (M3S) provides a framework to synchronize the experimental nature of machine learning and the deterministic side of traditional software engineering. However, understanding the framework's generality and limitations still requires further investigation. This paper compares the existing validation case study to a new case study of the OCT image diagnosis support system. The comparison between the two case studies shows M3S capability to handle variations in the nature of ML system analysis. However, the framework's capability to handle different ML tasks other than multi-class classification still requires further investigation.

Abstract Development of machine learning (ML) systems differs from traditional approaches. The probabilistic nature of ML leads to a more experimentative development approach, which often results in a disparity between the quality of ML models with other aspects such as business, safety, and the overall system architecture. Herein the Multi-view Modeling Framework for ML Systems (M3S) is proposed as a solution to this problem. M3S provides an analysis framework that integrates different views. It is supported by an integrated metamodel to ensure the connection and consistency between different models. To facilitate the experimentative nature of ML training, M3S provides an integrated platform between the modeling environment and the ML training pipeline. M3S is validated through a case study and a controlled experiment. M3S shows promise, but future research needs to confirm its generality.