The Google Prediction API provides a simple way for developers to create software that learns how to handle incoming data. For example, the Google-hosted algorithms could be trained to sort e-mails into categories for "complaints" and "praise" using a dataset that provides many examples of both kinds. Future e-mails could then be screened by software using that API, and handled accordingly.
Machine learning is not an easy feature to build into software. Different algorithms and mathematical techniques work best for different kinds of data. Specialized knowledge of machine learning is typically needed to consider using it in a product.
Google's service provides a kind of machine-learning black box-- ata goes in one end, and predictions come out the other. There are three basic commands: one to upload a collection of data, another telling the service to learn what it can from it, and a third to submit new data for the system to react to based on what it learned.
"Developers can deploy it on their site or app within 20 minutes," says Green. "We're trying to provide a really easy service that doesn't require them to spend month after month trying different algorithms." Google's black box actually contains a whole suite of different algorithms. When data is uploaded, all of the algorithms are automatically applied to find out which works best for a particular job, and the best algorithm is then used to handle any new information submitted.
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