Microsoft Azure ML
Microsoft Data Platform Insider Blog
Microsoft Big Data Solutions
Data Science Dojo
Azure Big Data Blog
This blog post is authored by Roger Barga, Group Program Manager for Microsoft Azure Machine Learning
Last month we announced Microsoft Azure Machine Learning and, this week, we made it available for public preview at our Worldwide Partner Conference 2014 (WPC). There was a lot of excitement and anticipation in our team leading up to the launch. We have worked closely with customers and partners in our Technical Advisory Program (TAP) and Private Preview program, listening to their feedback and adjusting our service accordingly. But there still was the open question of how it would be received by the general user community. In machine learning (ML) speak, we had great training data through our early private preview customers, but would our model generalize? That question was answered in short order.
Minutes after Scott Guthrie announced the public preview launch of Azure ML in his WPC opening keynote on Monday morning, our service meters signaled the first users had provisioned their own Azure ML workspace from the Azure Portal and had started running experiments. Momentum built throughout the first day and by day two of WPC on Tuesday, over 1,000 users had provisioned roughly 1,300 modeling workspaces on Azure ML, and these new users had built and run over 2,000 experiments and deployed over 50 ML web services on Azure.
These numbers were encouraging but there is nothing quite like hearing directly from our users. On Tuesday evening at a WPC social event, I met a data scientist from one of our partner companies. He shared with me that, upon hearing of Azure ML in the keynote on Monday, he had skipped all social events that evening and returned to his hotel room where he worked with Azure ML until the wee hours of the morning. He was thrilled with the service and noted that he had never been able to put a model into production so fast in his professional career.
It’s electrifying to see this level of passion and intellectual curiosity around data science and ML, as it is to see our customers using Azure ML to build and evaluate predictive models, run experiments, and then publish their model as a web service in minutes.
There was a lot of activity at our demo booth at WPC as attendees stopped by to learn more about Azure ML and to see the applications that our partners had built and deployed for their customers. If you wish to learn more or get started yourself you will find self-learning resources and a user forum on the Azure ML Central site.
And, something else I am very excited to share…
At the 2014 Microsoft Research Faculty Summit which took place in Redmond earlier this week, MSR announced a new program which will provide Azure ML access grants to both seasoned researchers and students. There are two flavor of these access grants. The first is a data science instructional award which will provide an individual account on Azure ML for each student in an intermediate or advanced data science class, along with 500 GB of cloud data storage for each student. The second is a research collaboration award which will provide a shared workspace on Azure ML, along with 10 TB of cloud data storage, to enable a group of researchers interested in hosting a data collection in Microsoft Azure ML to discover and share predictive models.
We look forward to seeing the data science courses that will use Azure ML, and the creative ML web services that students will build, and the research collaboration that spring up in the academic community around shared Azure ML workspaces. Read more about the MSR Azure ML grant program here.
Having returned from WPC, our team is now turning our collective attention to the road ahead. This is just the beginning for our new service. We look forward to seeing what exciting things our customers, partners and researchers in academia accomplish with Azure ML. We’ll listen closely to their feedback and requests while our service is in public preview. Just as an ML model never really ships, but rather it constantly improves over time with feedback and learning, our team will continue to refine and improve Azure ML in response to customer feedback and our own learnings while in public preview.
If you have not tried Azure ML yet, you can go ahead and get started right now. I also invite you to watch a video of selective partners talking about their Azure ML experience.
Happy modeling and let us know your thoughts – we are listening…
Comments in this blog are open and monitored for each post for a period of one week after the posting date. If you have a specific question about a blog post that is older than one week, please submit your question via our Twitter handle @MSCloud