Kenji Takeda, Solutions Architect and Technical Manager, Microsoft Research, writes:
And why not? Machine learning promises to transform our ability to understand, model, and predict the world around us. It is pushing boundaries in areas as diverse as image recognition, cardiac patient risk management, and real-time speech translation.
Despite an explosion in algorithm development, libraries, and services that has made machine learning more accessible, most researchers are not machine learning experts. It’s one thing to write a one-off model and tune it to your application—it is quite another to make that model repeatable and reliable for others to use. If only it were easier to build models and publish them straightaway, then maybe machine learning would be less impenetrable to the majority of researchers.
The recently launched Microsoft Azure Machine Learning (Azure ML) service hosted in the cloud aims to make it easier to build and deploy models for use in limitless applications. To explore this brave new world of machine learning, 50 researchers from 15 universities in France, Italy, the Netherlands, and the United Kingdom recently met at the Microsoft Research lab in Cambridge, England, eager to see how their work could be transformed.
Lab director Andrew Blake kicked off the day by highlighting some of the machine learning techniques that Cambridge lab researchers have used to develop body tracking with Kinect, TrueSkill player matching for Xbox, game and movie recommendation systems, and medical imaging methods that measure tumor growth. These achievements entailed hundreds of person years of effort, from fundamental research to applied algorithm development and implementation. Blake concluded by showing how this depth of knowledge is now available in Azure ML, for anyone to use in their own applications.
Participants then took on a hands-on journey through the web-based Azure ML Studio. Much of the work in building a predictive model involves the nitty-gritty of handling data ingress and the cleaning, slicing, and dicing of data sources of all kinds. Azure ML Studio makes this pipeline clear and easy to build, using a combination of graphical workflow and R scripts.
The real magic came at the end, when we published our best predictive model online with a mouse click—exposing it through a web application programming interface (API) instantly. This aroused great interest, as participants realized they could rapidly deploy and share models with no web API expertise.
Needless to say, we want the Azure ML platform to reach far more than 50 researchers, which is why we are offering substantial grants for researchers and data-science educators via the Microsoft Azure for Research Award program. The next deadline for grant proposals is November 15, 2014, with subsequent deadlines every two months thereafter. And, if you’re not sure but interested in testing this out, we offer a free tier of Machine Learning Studio where you can use up to 10GB of your own data. Whether you just want to get your feet wet or you are ready to dive in completely, we invite you to join the machine learning revolution. Azure ML might just transform your research.
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