Microsoft to provide drag-and-drop machine learning on Azure by Derrick Harris.
From the post:
Microsoft is stepping up its cloud computing game with a new service called Azure Machine Learning that users visually build and machine learning models, and then publish APIs to insert those models into applications. The service, which will be available for public preview in July, is one of the first of its kind and the latest demonstration of Microsoft’s heavy investment in machine learning.
Azure Machine Learning will include numerous prebuilt model types and packages, including recommendation engines, decision trees, R packages and even deep neural networks (aka deep learning models), explained Joseph Sirosh, corporate vice president at Microsoft. The data that the models train on and analyze can reside in Azure or locally, and users are charged based on the number of API calls to their models and the amount of computing resources consumed running them.
The reason why there are so few data scientists today, Sirosh theorized, is that they need to know so many software tools and so much math and computer science just to experiment and build models. Actually deploying those models into production, especially at scale, opens up a whole new set of engineering challenges. Sirosh said Microsoft hopes Azure Machine Learning will open up advanced machine learning to anyone who understands the R programming language or, really, anyone with a respectable understanding of statistics.
“It’s also very simple. My high school son can build machine learning models and publish APIs,” he said.
Reducing the technical barriers to use machine learning is a great thing. However, if that also results in reducing the understanding of machine learning, its perils and pitfalls, that is also a very bad thing.
One of the strengths of the Weka courses taught by Prof. Ian H. Witten is that students learn that choices are made in machine learning algorithms that aren’t apparent to the casual user. And that data choices can make as much different in outcomes as the algorithms used to process that data.
Use of software with no real understanding of its limitations isn’t new but with Azure Machine Learning any challenge to analysis will be met with the suggestion you “…run the analysis yourself.” Where the speaker does not understand that a replicated a bad result is still a bad result.
Be prepared to challenge data and means of analysis used in drag-n-drop machine learning drive-bys.