Enabling Automated Machine Learning for Model-Driven AI Engineering

• URL: http://arxiv.org/abs/2203.02927v1
• Date: Sun, 6 Mar 2022 10:12:56 GMT
• Title: Enabling Automated Machine Learning for Model-Driven AI Engineering
• Authors: Armin Moin, Ukrit Wattanavaekin, Alexandra Lungu, Moharram Challenger, Atta Badii, Stephan G\"unnemann
• Abstract summary: We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
• Score: 60.09869520679979
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Developing smart software services requires both Software Engineering and Artificial Intelligence (AI) skills. AI practitioners, such as data scientists often focus on the AI side, for example, creating and training Machine Learning (ML) models given a specific use case and data. They are typically not concerned with the entire software development life-cycle, architectural decisions for the system and performance issues beyond the predictive ML models (e.g., regarding the security, privacy, throughput, scalability, availability, as well as ethical, legal and regulatory compliance). In this manuscript, we propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems by choosing the most appropriate ML model, algorithm and techniques with suitable hyper-parameters for the task at hand. To validate our work, we carry out a case study in the smart energy domain.

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