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://aka.ms/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.

One of the promise of IoT is to allow bringing the intelligence of the Cloud to the Edge to run IoT data analytics as close as possible to the data source. This allows to reduce latencies, optimize performance and response times, support offline scenario, comply with privacy policies and regulations, reduce data transfer cost, and more…

One thing you really have to consider when bringing Artificial Intelligence to the edge is the hardware you will need to run these powerful algorithms. Ted Way from the Azure Machine Learning team joins Olivier on the IoT Show to discuss hardware acceleration at the Edge for AI. We will discuss scenarios and technologies Microsoft develops and uses to accelerate AI in the Cloud and at the Edge such as Graphic cards, FPGA, CPU,… To illustrate all this, Ted walks us through real life scenarios and demos IoT Edge running Machine Learning vision algorithms.

Learn more about hardware acceleration for AI at the Edge: https://docs.microsoft.com/azure/machine-learning/service/concept-accelerate-with-fpgas

Create a Free Account (Azure): https://aka.ms/aft-iot

Looking to get started deploying intelligence to the edge, or how you can scale up your edge intelligence testing? The new Azure Marketplace offer “Azure IoT Edge on Ubuntu” makes both easy. Learn more in this episode of the IoT Show with Greg Manyak, PM from the Azure IoT Platform show, and Olivier.