Microsoft acquires Express Logic, accelerating IoT development for billions of devices at scale


IoT sensors are being infused into just about everything, from industrial equipment to consumer devices, and increasingly these devices are connecting.

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Since the inception of Azure, we have been focused on delivering a true hybrid cloud where applications spanning public cloud and on-premises datacenters are built and run consistently. As organizations are now building applications that span the intelligent cloud and intelligent edge, the same approach is needed.

Fundamentally, the principles and technology needed for developing hybrid cloud applications are the same as intelligent cloud and intelligent edge applications. Azure’s longstanding leadership in hybrid cloud provides developers with unique know-how toward building modern applications that span the edge and the cloud.

Next week at Microsoft Build, more than 6,000 developers will join us in Seattle to experience the latest advancements in dev tools and cloud services. Today, to help usher in Build, I’m excited to share some of the new Azure innovations that we will be showcasing at Build that enable developers to build this new generation of hybrid applications with greater productivity and success.

To begin, we’re announcing several new AI services and capabilities that makes it easier for developers to build AI-powered applications. Furthering our commitment to building the most productive AI platform, we’re delivering key new innovations in Azure Machine Learning that simplify the process of building, training and deployment of machine learning models at scale. These include new automated machine learning advancements and an intuitive UI that make developing high-quality models easier, a new visual machine learning interface that provides a zero-code model creation and deployment experience using drag-and-drop capabilities and new machine learning notebooks for a rich, code-first development experience. Furthermore, new MLOps (DevOps for machine learning) capabilities with Azure DevOps integration provides developers with reproducibility, auditability and automation of the end-to-end machine learning lifecycle. To enable extremely low latency and cost-effective inferencing, we are also announcing the general availability of hardware-accelerated models that run on FPGAs, as well as ONNX Runtime support for NVIDIA TensorRT and Intel nGraph for high- speed inferencing on NVIDIA and Intel chipsets.

Azure Cognitive Services give connected devices, bots and apps the ability to see, hear, respond, translate, reason and more. Azure is the only public cloud that enables these Cognitive Services to be containerized to run on-premises, in the cloud and at the edge. Today we’re giving developers even more ways to create “smart” devices and services with a new Cognitive Services category called “Decision” that delivers users specific recommendations to enable informed and efficient decision-making. Azure Cognitive Services such as Content Moderator, the recently announced Anomaly Detector and a new preview service called Personalizer, which uses reinforcement learning to provide each user with a relevant experience to drive engagement, will be part of this new category. We’re also delivering several new services in public preview, including Ink Recognizer for embedding digital ink recognition capabilities; Form Recognizer for automating data entry by extracting text, key-value pairs and tables from documents; and new conversation transcription capability in Speech Service, which transcribes meeting conversations in real time so participants can fully engage in the discussion, know who said what when and quickly follow up on next steps.

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