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It seems that everybody today wants to employ machine learning in their research. 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.
—Kenji Takeda, Solutions Architect and Technical Manager, Microsoft Research
A year ago, the Microsoft Azure for Research project began as a small effort to help external researchers and scientists (and even Microsoft) understand how the cloud generally—and Microsoft Azure specifically—could accelerate research insights. Microsoft Azure for Research facilitates scholarly and scientific research by enabling researchers to take full advantage of the power and scalability of cloud computing for collaboration, computation, and data-intensive processing. Training events, online training, webcasts, and technical papers are just some of the resources the project provides to help researchers get up to speed with cloud computing.
The project also features an award program, which provides qualified research proposals with substantial grants of Microsoft Azure storage and compute resources for one year. The response to the award program has been overwhelming. In the past year, we have received more than 700 proposals, with submissions from all seven continents—yes, there was even one proposal from researchers in Antarctica!
I’m pleased to report that Microsoft Azure for Research has granted awards to more than half of the submitted project proposals, facilitating research in a wide range of disciplines, including computer science, biology, environmental science, genomics, and planetary science. The project clearly has tapped into the pent-up demand of researchers who want to focus their time and resources on solving complex problems rather than managing computing systems.
And while we’re still in the early days of this transition of research to the cloud, the first results are encouraging. To cite just a couple of cloud-enabled outcomes, we’ve seen urbanologists analyze big data to create new traffic-prediction models, and we’ve watched researchers from an array of disciplines work to unravel the effects of climate change on surface flooding via the National Flood Interoperability Experiment. The results of these projects and the other 360 that have received Microsoft Azure for Research grants demonstrate that Azure is a powerful resource for scholarly and scientific researchers.
If you have an idea for a cloud-enabled research project, we encourage you to apply for a Microsoft Azure for Research grant. The award program has a standing request for proposal (RFP) for any project that uses Microsoft Azure in research; these proposals are reviewed on the fifteenth of even-numbered months (February, April, June, August, October, and December). The program also issues special-opportunity RFPs, most of which have a set deadline for submission. Current special-opportunity RFPs and their deadlines include Azure Machine Learning (November 15, 2014), Climate Data (November 15, 2014), Food Resilience Climate Data (November 15, 2014), Celebration of Women in Computing (December 15, 2014), and Ebola Research (deadline is open-ended). Learn more about these RFPs.
—Dan Fay, Director, Microsoft Research
Massive Open Online Courses (MOOCs) have become one of the hottest trends in higher education, providing access to high-quality classes from such elite universities as Stanford and MIT. MOOCs thus offer a new opportunity for self-directed learning for millions of students worldwide. However, most MOOC platforms have been designed around the conventional classroom-learning model, with short lectures followed by a multiple-choice quiz that reinforces what has been taught during the video.
While this model works well for many subjects, it falls short in the sciences and engineering, where students need to conduct research experiments to support their study. Consequently, a major challenge in current MOOC systems is how to design a framework that effectively supports large-scale, self-directed learning as well as self-organized experiments and research. To meet this need, Beihang University, in collaboration with Microsoft Research Asia, has developed MOOR (Massive Open Online Research), a new cloud-based platform built on Microsoft Azure.
The MOOR platform consists of three major components:
The illustration below shows the relationship of these three pieces.
The MOOR platform consists of three components, each of which plays an essential role in enabling lab experiments and research in MOOCs.
Wenjun Wu, professor of computer science and the MOOR project leader at Beihang University, comments that “MOOR lets teachers design course-specific content and tasks and allows students to conduct research and creative experiments remotely—anywhere, anytime.” The MOOR system will make its debut during the autumn semester at Beihang’s MOOC Center, where it will be used in conjunction with an undergraduate course in computer networking.
As noted earlier, MOOR is powered by Microsoft Azure—for good reason. As a massive online learning environment, MOOR needs to push high-quality video streams to tens of thousands of students simultaneously, a need that demands a cloud-based, elastic video streaming service. With its almost limitless scalability, Azure easily supports large-scale media streaming of MOOC lecture videos. In addition, Azure provides virtual machine (VM) resources that permit customization of the software environment of the virtual labs; by defining VMs, instructors enable their students to run course-specific simulations and logic verifications and to access virtual lab equipment. Furthermore, Azure tools facilitate Python-based web portal development for MOOR and support load balancing for the web application.
Professor Wu and his team are continuing to collaborate with Microsoft Research Asia to enhance MOOR, which will be one of the featured demos at the Microsoft Research Asia Faculty Summit 2014 in Beijing from October 30 to 31. “We look forward to interacting with distinguished scientists and scholars from Microsoft Research and top Asian universities [at the summit],” said Professor Wu. “This is not only a great opportunity to let top-tier academics learn about our project, but also a chance for us to get their feedback and suggestions for future improvement, and an opening for exploring possible collaboration in the future.”
—Xin Ma, Senior Research Program Manager, Microsoft Research Asia