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.
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