Extensible Modeling Framework for Reliable Machine Learning System Analysis
2023 IEEE/ACM 2ND INTERNATIONAL CONFERENCE ON AI ENGINEERING - SOFTWARE ENGINEERING FOR AI, CAIN(2023)
摘要
Machine learning system analysis requires different approaches for each different task and domain. Selecting a proper set of analytic models can be challenging for a specific problem. This paper discusses the extensibility of the Multi-View Modeling Framework for ML Systems approach using process mapping and extensible metamodel. We conducted a case study to evaluate the feasibility of such extensibility by extending the approach to facilitate an activity-driven analysis for an optical character recognition system. Based on the result of the case study, we found that Multi-View Modeling Framework for ML Systems is likely to be extensible.
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关键词
machine learning,system analysis,extensibility
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