  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.
