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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 Statement
Copyright 2016 The authors
Official URL: http://proceedings.mlr.press/v50/montgomery15.html
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 and systems software |
| Research Field: | Service oriented computing |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Information systems, technologies and services not elsewhere classified |
| UTAS 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: | 2020-04-27 |
| Downloads: | 0 |
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