Ease.ml in action
| Autor: | Bojan Karlas, Ce Zhang, Ji Liu, Wentao Wu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Service (systems architecture)
Computer science Model selection General Engineering 02 engineering and technology Action (philosophy) Human–computer interaction 020204 information systems 0202 electrical engineering electronic engineering information engineering Key (cryptography) 020201 artificial intelligence & image processing User interface Declarative learning Host (network) |
| Zdroj: | Proceedings of the VLDB Endowment. 11:2054-2057 |
| ISSN: | 2150-8097 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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