At Connect(); 2017, Microsoft today introduced a number of new tools for developers. The big focus this year is on artificial intelligence, and Microsoft is launching a new extension for Visual Studio that will help developers integrate AI into their apps.
Microsoft’s new Visual Studio Tools for AI extension lets users develop, debug, and deploy deep learning models in Visual Studio. The extension comes with support for the major machine learning frameworks, including Microsoft’s Cognitive Toolkit, Google TensorFlow, Caffe2, and MXNet. Using this new extension, developers will be able to develop deep learning models within Visual Studio, which can later be debugged right from the IDE itself. Developers would then be able to take advantage of the power of Azure Batch AI to train their deep learning models and deploy them in production using Azure Machine Learning. Visual Studio will also offer developers with a gallery of sample deep learning models which developers can play around with in order to build and test their models.
“With today’s intelligent cloud, emerging technologies like AI have the potential to change every facet of how we interact with the world. Developers are in the forefront of shaping that potential. Today at Connect(); we’re announcing new tools and services that help developers build applications and services for the AI-driven future, using the platforms, languages and collaboration tools they already know and love,” Microsoft’s EVP Scott Guthrie said in a statement.
Microsoft’s also bringing AI to embedded devices with the first ever beta release of Azure IoT Edge, which lets developers enable artificial intelligence and advanced analytics capabilities on the Internet of Things devices. Microsoft’s AI push is enormous for the company’s cloud business, and extensions like Visual Studio Tools for AI that simplify the engineering process for developers will be crucial when it comes to speeding up the adoption of AI.
<p>I admit I have no experience using any of these tools, but I find myself wondering if they're adequate for developing brand-new AI capabilities or just for "mining" well-known areas like speech-recognition. I have some very dated experience using Neural Nets to develop a medical product and my experience suggests that the access to tools is the easiest part. BTW, the medical product failed to live up to expectations.</p>