By Travis Green of the Google Prediction API Team.

If you’re looking to make your app smarter and you think machine learning is more complicated than making three API calls, then you’re reading the right blog post.

Today, we are releasing v1.2 of the Google Prediction API, which makes it even easier for preview users to build smarter apps by accessing Google’s advanced machine learning algorithms through a RESTful web service.

Some technical details of the Prediction API:
  • Chooses best technique from several available machine learning algorithms.
  • Supported inputs: numeric data and unstructured text.
  • Outputs hundreds of discrete categories, or continuous values.
  • Integrates with many platforms: Google App Engine, web and desktop apps, and command line.
  • v1.2 improvements:
    • Simpler interface: automatic data type detection, and score normalization.
    • Paid usage tier.
    • Improved usage monitoring and faster signup through the APIs Console.
Ideas to make the most of the Prediction API:
  • Recommendation: What products might a user be interested in? (example)
  • Filter RSS feeds, user comments, or feedback: Which posts are most relevant? Should a user comment be featured? Which feedback should we look at first? (example)
  • Customize homepages: Predict what content a user would like to see and populate the page with the user’s anticipated interests.
  • Sentiment analysis: Is this comment positive or negative? Does a commenter support Group A or Group B?
  • Message routing: Route emails to the appropriate person based on analysis of the email contents.
  • See the Prediction API website for many more!
To join the preview group, go to the APIs Console and click the Prediction API slider to “ON,” and then sign up for a Google Storage account.

We would also like to continue to thank our supportive preview users for their help making the API the service it is today. We look forward to seeing many more of you join us in making the web just a little bit smarter, and hearing your thoughts and feedback through our discussion group.

Travis Green's favorite part about his job is designing smart applications. In his spare time, he is in the great outdoors (looking for trouble).

Posted by Scott Knaster, Editor