Ease.ml in Action: Towards Multi-tenant Declarative Learning Services - Karlas, Bojan; Liu, Ji; Wu, Wentao; Zhang, Ce (Proceedings of the VLDB Endowment (VLDB 2018), 2018)
We demonstrate ease.ml, a multi-tenant machine learning service we host at ETH Zurich for various research groups. Unlike existing machine learning services, ease.ml presents a novel architecture that supports multi-tenant, cost-aware model selection that optimizes for minimizing total regrets of all users. Moreover, it provides a novel user interface that enables declarative machine learning at a higher level: Users only need to specify the input/output schemata of their learning tasks and ease.ml can handle the rest.In this demonstration, we present the design principles of ease.ml, highlight the implementation of its key components, and showcase how ease.ml can help ease machine learning tasks that often perplex even experienced users.