Google's cloud-based machine learning tools
developers.google.com
developers.google.com
100 predictions a day isn't enough to test if it works. Somebody needs to make a much bigger commitment of their time to make a machine learning application that really works -- when you look at what's holding machine learning back it's not the algorithms or the hardware requirements, it's that people don't want to do the work of creating high quality training sets and validating them.
* Training models can be rather computationally expensive. Especially if your business requires training new models very often, this can be prohibitively expensive to do in ec2, whereas the prediction API solves that for you.
* Just hooking up to an open source ML library isn't the whole story. You still need to do backtesting on different algorithms and do the parameter tuning, aka you need some machine learning know-how. The Prediction API does all this for you automatically and probably uses a much larger set of algorithms than you would bother to test yourself.
1.2. From Customer to Google. By submitting, posting or displaying any Customer Data on or through the Service, Customer gives Google a worldwide, non-sublicensable, non-transferable, non-exclusive, terminable, limited license to reproduce, adapt, modify, translate, publish, publicly perform, publicly display and distribute any Customer Data for the sole purpose of enabling Google to provide Customer with the Service in accordance with the Agreement.
I've found the following (it's about photographs and social networks) quite helpful in explaining the issues: http://www.readwriteweb.com/archives/getty_images_says_googl...
http://www.readwriteweb.com/hack/2012/02/three-new-tools-bri...