While you've hopefully enjoyed a relaxing summer, we here at BigML have been hard at work—adding new features, growing the team, and establishing partnerships to help you be more successful with your predictive modeling activities.  The introduction of subscription plans gives you the ability to perform unlimited tasks with your data at 64MB, 1GB and 4GB levels, starting as low as $30/mo and even cheaper for quarterly, yearly or student/non-profit usage.

Node.js is a software platform that is used to build scalable network and server-side applications—we're excited to bring the power of Machine Learning to the node.js community through the release of our node.js bindings.  This allows node.js developers to create low-latency, event-driven applications that use our Machine Learning back end to add predictive attributes and capabilities to their projectwhich will be especially powerful when coupled with our forthcoming high-performance prediction service!

You can download the library from github, learn more from our documentation, or email us with any questions.

BigML worked with import.io's (www.import.io) web crawler browser to pull data from nearly 60,000 Etsy stores into a single dataset, which we then imported into BigML to help predict likely sales from an Etsy store based on variables such as number of items offered, number of admirers, year of store establishment and amount of customer feedback.

You can view the model to see the results and start building your own predictions.  Or learn more about the work BigML did with import.io in our blog post.

Machine learning papers typically proceed as follows: find a dataset for a problem that is only partially solved, invent or improve an algorithm to solve this problem such that some improvement on some standard metric is made on that dataset, write it up and publish. Repeat until tenured. While the above approach produces a substantial number of computer science professors, it isn't nearly as successful at getting machine learning algorithms in a position to make a measurable difference in society at large. However, Dr. Kiri Wagstaff sees things very differently. She published a very inspirational paper at the 2012 ICML conference that laid out a program of challenges for machine learning researchers interested in making a difference in the real world. Inspired by that paper, our machine learning guru, Dr. Charlie Parker, wrote this great blog post.

BigML is very pleased to welcome David Gerster to the team.  As BigML's VP of Data Science, David engages with a variety of customers and partners to ensure that BigML's product offerings are fully representative of customer requirements.

Prior to joining BigML, David was Director of Data Science at Groupon.  While at Groupon, David used BigML to help predict usage of Groupon's mobile application.  As David likes to tell people, he liked BigML so much that he decided to join us!

The BigML team is hard at work on some killer new features and functionality which we plan on rolling out to you over the next few months.  Keep your eyes and ears peeled for support for text processing, workgroups, high-performance predictions, time-series analysis, and added predictive model visualizations.