From the post:
At BigML we believe that over the next few years automated, data-driven decisions and data-driven applications are going to change the world. In fact, we think it will be the biggest shift in business efficiency since the dawn of the office calculator, when individuals had “Computer” listed as the title on their business card. We want to help people rapidly and easily create predictive models using their datasets, no matter what size they are. Our easy-to-use, public API is a great step in that direction but a few bindings for popular languages is obviously a big bonus.
Thus, we are very happy to announce an open source Python binding to BigML.io, the BigML REST API. You can find it and fork it at Github.
The BigML Python module makes it extremely easy to programmatically manage BigML sources, datasets, models and predictions. The snippet below sketches how you can create a source, dataset, model and then a prediction for a new object.
The “business efficiency” argument sounds like the “paperless office” to me.
Certain we will be able to do different, interesting and quite possibly useful things with machine learning and data. That we will become more “efficient,” is a separate question. By what measure?
If you look at scholarship from the 19th century, where people lacked many of the time saving devices of today, you will find authors who published hundreds of books, not articles, books. And not short books either. Were they more “efficient” than we are?
Rather than promise “efficiency,” promote machine learning as a means to do a particular task and do it well. If there is interest in the task and/or the result, that will be sufficient without all the superlatives.