  In recent years, Web services are becoming more and more intelligent (e.g.,
in understanding user preferences) thanks to the integration of components that
rely on Machine Learning (ML). Before users can interact (inference phase) with
an ML-based service (ML-Service), the underlying ML model must learn (training
phase) from existing data, a process that requires long-lasting batch
computations. The management of these two, diverse phases is complex and
meeting time and quality requirements can hardly be done with manual
approaches.
  This paper highlights some of the major issues in managing ML-services in
both training and inference modes and presents some initial solutions that are
able to meet set requirements with minimum user inputs. A preliminary
evaluation demonstrates that our solutions allow these systems to become more
efficient and predictable with respect to their response time and accuracy.
