标题： 为模型驱动的人工智能工程提供自动化机器学习功能 显示英文标题

标题： Enabling Automated Machine Learning for Model-Driven AI Engineering

摘要： 开发智能软件服务需要软件工程和人工智能（AI）技能。 AI从业者，例如数据科学家，通常专注于AI方面，例如在给定特定用例和数据的情况下创建和训练机器学习（ML）模型。 他们通常不关心整个软件开发生命周期、系统的架构决策以及预测性ML模型之外的性能问题（例如，关于安全、隐私、吞吐量、可扩展性、可用性，以及伦理、法律和监管合规性）。 在本文中，我们提出了一种新方法，以实现模型驱动的软件工程和模型驱动的人工智能工程。 特别是，我们支持自动化机器学习，从而帮助没有深厚AI知识的软件工程师通过为当前任务选择最合适的ML模型、算法和技术以及合适的超参数来开发AI密集型系统。 为了验证我们的工作，我们在智能能源领域进行了一项案例研究。 显示英文摘要

摘要： 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.