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.

本文提出了一种新方法，支持自动化机器学习，帮助软件工程师在不需要深入了解人工智能的情况下，选择适当的机器学习模型、算法和技术以及适当的超参数来开发人工智能密集型系统，并进行智能能源领域的案例研究。