Ease.ml in Action: Towards Multi-tenant Declarative Learning Services

@article{Karlas2018EasemlIA,
  title={Ease.ml in Action: Towards Multi-tenant Declarative Learning Services},
  author={Bojan Karlas and J. Liu and Wentao Wu and Ce Zhang},
  journal={Proc. VLDB Endow.},
  year={2018},
  volume={11},
  pages={2054-2057}
}
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… Expand
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