AI News, Standardizing the World of Machine Learning Web Service APIs

Standardizing the World of Machine Learning Web Service APIs

But this variety works against more widespread adoption of ML techniques, since any potential new user (individual or organisation) must invest considerable time into any one toolkit they choose to use.

Like many other web services in the last decade, many of these ML services claim to have RESTful APIs, although these claims are often fairly weak and boil down to the fact that they communicate over HTTP and use the widely-understood JSON data exchange format.

Each resource uses schema to describe its characteristics: dataset attributes can describe the data format they produce, learners can describe the structure of leaning tasks they can process, predictors can describe the types of data they accept and the form of the predictions they make.

And as an open specification, the PSI API can be implemented by anyone: a large company could provide a full-featured ML service, a research team could provide datasets, an individual could provide a single predictor, and all these resources can interoperate.

We don’t hold that RESTfulness is a goal in its own right, instead that the API has been designed to enable automated discovery of a PSI service’s data, learners and predictors, flexible customisation of client controls without prior knowledge of the learner or predictor being used, and extension of the API by allowing services to provide links to related resources in a manner similar to the link element of HTML.

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