psi.cecs.anu.edu.au

Machine Learning as a Service

Protocols and Structures for Inference. Machine Learning as a Service. Protocols and Structures for Inference. PSI) project has developed an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers. Each has its own API and is tailored to suit a different subset of machine learning activities.

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Machine Learning as a Service | psi.cecs.anu.edu.au Reviews

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Protocols and Structures for Inference. Machine Learning as a Service. Protocols and Structures for Inference. PSI) project has developed an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers. Each has its own API and is tailored to suit a different subset of machine learning activities.

<META> KEYWORDS

1 using psi

2 specification

3 accessibility

4 interoperability

5 web services

6 web resources

7 federated

8 client

9 and their souces

10 live on

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Machine Learning as a Service | psi.cecs.anu.edu.au Reviews

https://psi.cecs.anu.edu.au

Protocols and Structures for Inference. Machine Learning as a Service. Protocols and Structures for Inference. PSI) project has developed an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers. Each has its own API and is tailored to suit a different subset of machine learning activities.

INTERNAL PAGES

psi.cecs.anu.edu.au

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PSI Draft Specification

http://psi.cecs.anu.edu.au/spec

Mark Reid, James Montgomery, Barry Drake, Avraham Ruderman. Version 2. 13 August 2013. Modelling Machine Learning Problems. Choice of Architectural Style. Choice of Instance Representation. Use of Attributes and Schema. RESTfulness of the Architecture. The PSI Service Architecture. Discovering a PSI Service. Discovering Related Services and Actions. Selecting Subsets of Instances. Join with a Transformer. Tasks and Task Schema. Minimum Requirements to be a PSI Service. Discovering a PSI service. Since ne...

2

Using PSI

http://psi.cecs.anu.edu.au/examples/index.html

Protocols and Structures for Inference. I want to…. 8230;use a PSI service. Work through the examples. On this site and refer to the PSI Architecture. Section of the specification. For details of the JSON format of requests and responses. A summary of the REST API. Is also available. Each example below refers to relevant sections of the specification. Discover what resources are available at a PSI service. Examine a relation (dataset) and access its contents using an attribute. Refer to the specification.

3

PSI Draft Specification

http://psi.cecs.anu.edu.au/spec/index.html

Mark Reid, James Montgomery, Barry Drake, Avraham Ruderman. Version 2. 13 August 2013. Modelling Machine Learning Problems. Choice of Architectural Style. Choice of Instance Representation. Use of Attributes and Schema. RESTfulness of the Architecture. The PSI Service Architecture. Discovering a PSI Service. Discovering Related Services and Actions. Selecting Subsets of Instances. Join with a Transformer. Tasks and Task Schema. Minimum Requirements to be a PSI Service. Discovering a PSI service. Since ne...

4

Using PSI

http://psi.cecs.anu.edu.au/examples

Protocols and Structures for Inference. I want to…. 8230;use a PSI service. Work through the examples. On this site and refer to the PSI Architecture. Section of the specification. For details of the JSON format of requests and responses. A summary of the REST API. Is also available. Each example below refers to relevant sections of the specification. Discover what resources are available at a PSI service. Examine a relation (dataset) and access its contents using an attribute. Refer to the specification.

5

PSI Client

http://psi.cecs.anu.edu.au/demo

Protocols and Structures for Inference — PSI Explorer. View JSON messages sent between client and server.

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Machine Learning as a Service

Protocols and Structures for Inference. Machine Learning as a Service. Protocols and Structures for Inference. PSI) project has developed an architecture for presenting machine learning algorithms, their inputs (data) and outputs (predictors) as resource-oriented RESTful web services in order to make machine learning technology accessible to a broader range of people than just machine learning researchers. Each has its own API and is tailored to suit a different subset of machine learning activities.

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