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Protocols and structures for inference: a RESTful API for machine learning

Citation

Montgomery, J and Reid, MD and Drake, B, Protocols and structures for inference: a RESTful API for machine learning, Proceedings of Machine Learning Research (PMLR) - Volume 50: 2nd Conference on Predictive APIs and Apps (PAPIs '15), 6-7 August 2015, Sydney, Australia, pp. 29-42. ISSN 1532-4435 (2016) [Refereed Conference Paper]


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Copyright 2016 The authors

Official URL: http://www.papis.io/blog/2015/6/2nd-international-...

Abstract

Diversity in machine learning APIs (in both software toolkits and web services), works against realising machine learning’s full potential, making it difficult to draw on individual algorithms from different products or to compose multiple algorithms to solve complex tasks. This paper introduces the Protocols and Structures for Inference (PSI) service architecture and specification, which presents inferential entities - relations, attributes, learners and predictors - as RESTful web resources that are accessible via a common but flexible and extensible interface. Resources describe the data they ingest or emit using a variant of the JSON schema language, and the API has mechanisms to support non-JSON data and future extension of service features.

Item Details

Item Type:Refereed Conference Paper
Keywords:machine learning, REST, RESTful, web service, predictive API
Research Division:Information and Computing Sciences
Research Group:Distributed Computing
Research Field:Web Technologies (excl. Web Search)
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
Author:Montgomery, J (Dr James Montgomery)
ID Code:101369
Year Published:2016
Deposited By:Information and Communication Technology
Deposited On:2015-06-19
Last Modified:2018-01-16
Downloads:0

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