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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://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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