Google Prediction API: Machine Learning Black Box Cloud Academy

This is my third article on how to build Machine Learning models in the Cloud. Google Prediction API, on the other hand, was released all the way back in 2011, and offers a very stable and simple way to train Machine Learning models via a RESTful interface, although it might seem less friendly if you generally prefer browser interfaces. I am not going to explore the wide range of services offered by Google Cloud Platform, you can easily check the Developers Console out by yourself for free, sign up for the Free Trial offered by Google ( 300 in credit to use for 2 months), and check out Cloud Academy's courses on Google Cloud Platform. We can define Google's approach as a "black box", since you get no control over what happens under the hood: your model configuration is restricted to specifying "Classification" vs. "Regression," or providing a preprocessing PMML (Predictive Model Markup Language) file and a set of weighting parameters in the case of categorical models. On the other hand, your input features (your columns) can contain any type of data, although certain types are easier to work with (i.e.