  Intelligent applications based on machine learning are impacting many parts
of our lives. They are required to operate under rigorous practical constraints
in terms of service latency, network bandwidth overheads, and also privacy. Yet
current implementations running in the Cloud are unable to satisfy all these
constraints. The Edge-Cloud Collaborative Intelligence (ECCI) paradigm has
become a popular approach to address such issues, and rapidly increasing
applications are developed and deployed. However, these prototypical
implementations are developer-dependent and scenario-specific without
generality, which cannot be efficiently applied in large-scale or to general
ECC scenarios in practice, due to the lack of supports for infrastructure
management, edge-cloud collaborative service, complex intelligence workload,
and efficient performance optimization. In this article, we systematically
design and construct the first unified platform, ACE, that handles
ever-increasing edge and cloud resources, user-transparent services, and
proliferating intelligence workloads with increasing scale and complexity, to
facilitate cost-efficient and high-performing ECCI application development and
deployment. For verification, we explicitly present the construction process of
an ACE-based intelligent video query application, and demonstrate how to
achieve customizable performance optimization efficiently. Based on our initial
experience, we discuss both the limitations and vision of ACE to shed light on
promising issues to elaborate in the approaching ECCI ecosystem.
