Microsoft showcases its edgy AI toolkit at Connect(); 2017mengjiao.fu
At the ongoing Microsoft Connect(); 2017, Microsoft has unveiled their latest innovations in AI development platforms. The Connect(); conference this year is all about developing new tools and cloud services that help developers seize the growing opportunity around artificial intelligence and machine learning. Microsoft has made two major announcements to capture the AI market.
Visual Studio Tools for AI
Microsoft has announced new tools for its Visual Studio IDE specific for building AI applications. Visual Studio Tools for AI is currently in the beta stage and is an extension to the Visual Studio 2017. It allows developers, data scientists, and machine learning engineers to embed deep learning models into applications. They also have built-in support for popular machine learning frameworks such as Microsoft Cognitive Toolkit (CNTK), Google TensorFlow, Caffe2, and MXNet.
It also comes packed with features such as custom metrics, history tracking, enterprise-ready collaboration, and data science reproducibility and auditing.
Visual Studio Tools for AI allows interactive debugging of deep learning applications with built-in features like syntax highlighting, IntelliSense and text auto formatting.
Training of AI models on the cloud is also possible using the integration with Azure Machine Learning. This integration also allows deploying a model into production.
Visualization and monitoring of AI models is available using TensorBoard, which is an integrated open tool and can be run both locally and in remote VMs.
Azure IoT Edge
Microsoft sees IoT as a mission-critical business asset. With this in mind, they have developed a product for IoT solutions. Termed as Azure IoT Edge, it enables developers to run cloud intelligence on the edge of IoT devices.
Azure IoT Edge can operate on Windows and Linux as well as on multiple hardware architectures (x64 and ARM). Developers can work on languages such as C#, C and Python to deploy models on Azure IoT Edge.
The Azure IoT edge is a bundle of multiple components. With AI Toolkit, developers can start building AI applications. With Azure Machine learning, AI applications can be created, deployed, and managed with the toolkit on any framework. Azure Machine Learning also includes a set of pre-built AI models for common tasks.
In addition, using the Azure IoT Hub, developers can deploy Edge modules on multiple IoT Edge devices.
Using a combination of Azure Machine Learning, Azure Stream Analytics, Azure Functions, and any third-party code, a complex data pipeline can be created to build and test container-based workloads. This pipeline can be managed using the Azure IoT Hub.
The customer reviews on Azure IoT edge have been positive up till now. Here’s what Matt Boujonnier, Analytics Application Architect at Schneider Electric says:
“Azure IoT Edge provided an easy way to package and deploy our Machine Learning applications. Traditionally, machine learning is something that has only run in the cloud, but for many IoT scenarios that isn’t good enough, because you want to run your application as close as possible to any events. Now we have the flexibility to run it in the cloud or at the edge—wherever we need it to be.”
With the launch of these two new tools, Microsoft is catching up quickly with the likes of Google and IBM to capture the AI market and providing developers with an intelligent edge.