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

conference contribution
posted on 2023-05-23, 10:09 authored by James MontgomeryJames Montgomery, Reid, MD, Drake, B
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.

History

Publication title

Proceedings of Machine Learning Research (PMLR) - Volume 50: 2nd Conference on Predictive APIs and Apps (PAPIs '15)

Volume

50

Editors

L Dorard, MD Reid & FJ Martin

Pagination

29-42

ISSN

1532-4435

Department/School

School of Information and Communication Technology

Publisher

Microtome Publishing

Place of publication

Brookline, MA USA

Event title

2nd International Conference on Predictive APIs and Apps (PAPIs '15)

Event Venue

Sydney, Australia

Date of Event (Start Date)

2015-08-06

Date of Event (End Date)

2015-08-07

Rights statement

Copyright 2016 The authors

Repository Status

  • Restricted

Socio-economic Objectives

Information systems, technologies and services not elsewhere classified

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