August 22, 2010

Google Prediction API

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The Prediction API enables access to Google's machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google's infrastructure and scale effortlessly as your data grows in size and complexity.

The Prediction API can be used in a variety of contexts. Application developers who want to predict new results from historic data can benefit from prediction capabilities. Example uses include user sentiment analysis, language identification, product recommendation, message routing, and fraud detection, among others.

The Prediction API supports CSV formatted training data, up to 100M in size. Numeric or unstructured text can be sent as input features, and discrete categories (up to a few hundred different ones) can be provided as output labels. * Google heavily uses predictive models for many tasks such as spam filtering, language identification, and objectionable content identification.

* Training time varies with the size of the training data, but testing has shown training times ranging from a few seconds to over an hour.

* Prediction can be performed in real-time, usually less than a second.

* You cannot currently download your model.

* The goal of the preview period is to collect feedback from a diverse range of developers. Participants will be selected from existing users of Google developer services as well as those who sign up on the waiting list.

Getting Started with the API

Here are the getting started instructions

You can access the Prediction API many different ways through its RESTful interface; however, the examples in this Getting Started Guide show you how to access the API by using simple UNIX shell commands. To learn about the other ways you can access the Prediction API, see the Developer's Guide, and our Third Party Libraries page.

Step 1—Upload

1. Check that your data is formatted for training.

Training data uploads currently must follow CSV format with each line as one datapoint with the first column representing the parameter to be predicted, and the remaining parameter as prediction signals. A language categorization dataset for English, Spanish, and French based off selected sentences from great works in each language can be found here.

2. Upload your data to Google Storage for Developers.

For more information on how to request a Google Storage for Developers account and how to upload data, please see the Google Storage for Developers Getting Started Guide.

While there are many ways to access Google Storage for Developers, the Google Storage for Developers manager graphical interface is the simplest. First, create and name a bucket, which will be referred to in the following examples as {$mybucket}, click on that bucket and upload your data to that bucket, shown below as ${mydata}.



Step 2—Train

1. Request an authentication token from Google.
2. Extract the "auth" variable.
3. Invoke the train mechanism.
4. Confirm model training is finished


Step 3—Predict

1. Format input as a JSON request.
2. Invoke the query mechanism.
3. Extract and use the response in your application.



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