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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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Machine Learning as a Service | psikit.net 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.

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2 specification

3 accessibility

4 interoperability

5 web services

6 web resources

7 federated

8 client

9 and their sources

10 live on

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Machine Learning as a Service | psikit.net Reviews

https://psikit.net

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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Protocols and Structures for Inference. Protocols and Structures for Inference. Project was a collaboration between the Australian National University. And Canon Information Systems Research Australia (CiSRA). The project team consisted of:. While the formal project ended in October 2013, the specification. Will continue to evolve, and the demonstration service. Protocols and Structures for Inference. Project received funding from the Australian Research Council. 8217;s Linkage Projects.

2

Using PSI

http://www.psikit.net/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.

3

Using PSI

http://www.psikit.net/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.

4

PSI Draft Specification

http://www.psikit.net/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...

5

PSI Draft Specification

http://www.psikit.net/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...

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psikit.net

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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