SQL Server started as a departmental database engine. It is now a modern data platform and a force in the industry.

In this episode of Data Exposed with Bob Ward, learn how Microsoft is innovating SQL Server from IOT edge devices to your data center to the Azure cloud.

Edge computing can solve specific business problems that demand some combination of in-house computing, high speed, and low latency that cloud-based AI can’t deliver, explained Deepu Talla, NVIDIA VP and GM of Embedded and Edge Computing.

The hardware and architecture that can support edge computing has improved significantly over the past year, including GPUs with Tensor Cores for dedicated AI processing, plus secure, high-performance networking gear. And edge server software is growing more sophisticated as well, such as NVIDIA’s EGX cloud-native software stack, which brings traditional cloud capabilities to the edge of the network. He also pointed to the company’s industry-specific application frameworks such as Metropolis for smart cities, Clara for health care, Jarvis for conversational AI, Isaac for robotics, and Aerial for telecommunications — each supporting forms of AI on NVIDIA GPUs.

Mahesh Yadav, Software Engineer on the Intelligent Edge team, joins the IoT Show to unbox the Microsoft Vision AI DevKit (aka.ms/iotshow/visionaidevkit), a smart camera for the intelligent edge.

The developer kit uses the Qualcomm’s Vision Intelligence 300 Platform which uniquely runs machine learning with hardware acceleration delivering results in milliseconds which is perfect for connected car or connected factory scenarios where you need low latency as well as support offline scenarios.

In this episode, you will see how easy it is to bring up AI on the edge with Azure IoT Edge and Azure Machine Learning.

The DevKit includes a sample AI model that identifies 183 objects including people, laptops, chairs and more. The highlight of the show is a real-time camera demo that asserts that both Mahesh and Olivier really are people.

And it’s always good when an AI affirms your personhood. 😉

ExplainingComputers explores Edge computing definitions and concepts.

This non-technical video focuses on edge computing and cloud computing, as well as edge computing and the deployment of vision recognition and other AI applications.

Also introduced are mesh networks, SBC (single board computer) edge hardware, and fog computing.

I’ve often referred to edge computing in many posts, but here’s a great article on why it will revolutionize IoT and help it really transform entire industries along with our everyday lives.

The edge is where data gets generated, events occur, things and people interact. The key is putting intelligence there. The Internet of Things (IoT) holds great promise for improving operational efficiencies and vastly reducing costly downtime. But for IoT to realize its potential, computational challenges must be overcome. Even […]

IoT is a technology paradigm that involves the use of internet connected devices to publish data often in conjunction with real-time data processing, machine learning, and/or storage services. Development of these systems can be enhanced through application of modern DevOps principles which include tasks like automation, monitoring, and all steps of the software engineering process from development, testing, quality assurance, and release. This video examines these concepts as they relate to IoT Edge Solutions using Azure DevOps, Application Insights, Azure Container Registries, containerized iot edge devices and Azure Kubernetes Service to create an end-to-end pipeline which deploys, smoke tests, and allows for scalable integration testing using replica sets in k8s.

Securing IoT, especially the intelligent edge, is a tall challenge that is best deliverable through a transparent community approach of unifying value contributions from various technology expertise to include but not limited to secure chip technologies, cryptography, software security engineering, and secure device engineering. The IoT Edge security model encourages this transparent community approach where we invite the experts to join us in engineering a safe and secure IoT.

Is this episode of the IoT Show, Eustace Asanghanwa, security PM in the Azure IoT team, walks us through the Azure IoT Edge security model and describes the Azure IoT Edge Security Manager.

Tune in on 6/19th at 9AM PT (or watch on demand after) for a live IoT Show Deep Dive on the topic: https://aka.ms/iotshow/deepdive/005

Learn more about the Azure IoT Edge Security Manager:
https://azure.microsoft.com/blog/securing-the-intelligent-edge/ https://docs.microsoft.com/azure/iot-edge/security

Try Azure IoT for free today: https://aka.ms/aft-iot

IoT will be the next driver of AI innovation. By 2025, there will be 55 billion IoT devices (Business Insider Intelligence), and  Due to to latency, cost, privacy and connectivity issues, being able to analyze data at the edge where it’s created is critical because it improves the speed of analysis and decision-making.

Data analytics has generally relied on human-defined classifiers or “feature extractors” which are rules that can be as simple as a linear regression, to more complicated machine learning algorithms. But can you imagine building a human-defined perfect rule-based system to model everything?

Last week, Microsoft announced the that Azure Data Box Edge  has gone GA.  Azure Data Box Edge is a hybrid cloud platform that brings compute and storage closer to the data source.

Forbes has a nice write up on the technology and why it’s crucial to hybrid cloud deployments.

Azure Data Box Edge is the cornerstone of Microsoft’s hybrid cloud platform. It plays a crucial role in the “intelligent cloud and intelligent edge” strategy of the company. The product belongs to the Azure Data Box portfolio that offers both online and offline solutions for transferring bulk data to the cloud.