Because of Azure Sphere Security Service, a.k.a. “AS3” (aka.ms/iotshow/azuresphere), Azure Sphere devices are more secure when they’re connected.

Caitie McCaffrey, leading the Azure Sphere Security Service engineering team joins us on the IoT Show to tell us what her team delivers and why device developers should care.

Learn more about Azure Sphere and its Security Service visiting https://aka.ms/iotshow/azuresphere

The Raspberry Pi is widely prized for its project capabilities among engineers and makers alike. It has been used to create everything from robots to remote monitoring devices since its release back in 2012. Not long after, industry managers took note of the tiny board’s capabilities and adapted them for use in manufacturing and automation operations, with some using the famous board to build specialized equipment to make those applications more efficient and cost-effective.

It’s been used to create everything from robots to remote monitoring devices since its release back in 2012.

Industry managers took note of the tiny board’s capabilities and adapted them for use in manufacturing and automation operations, with some using the famous board to build specialized equipment to make those applications more efficient and cost-effective.

On its own, the Raspberry Pi provided an attractive alternative to PLCs (Programmable Logic Controllers), which are prominent in many industries, including manufacturing, automation, and IIoT platforms. Being cost-effective and efficient at carrying out programmed tasks via a simple GPIO interface, allowed the Raspberry to break open the doors to those industries and be used for general operations. The board’s use in an industrial setting was so widespread that the Foundation designed a dedicated board based on the Pi for the sole purpose of being implemented into industrial operations. Thus the Raspberry Pi Compute Module was born.

Many of today’s smart products are reliant on processing in the cloud and the growing adoption of natural voice interfaces, imaging and presence detection, for example, not only raise performance issues but will create further challenges in the form of reliability, privacy and cost.

According to market research, by 2025 there is expected to be 65 billion connected devices generating 180 zetabytes of data, all of which will require complex and diverse processing capabilities.

This will be a massive opportunity.

“There is a huge market opportunity for a device that is able to address the needs of a range of applications delivering both performance and functionality while, at the same time, offering ease of use, low power and real-time operation.

QuickLogic Corporation and Antmicro jointly-announced QuickFeather, a small form factor development board designed to enable the next generation of low-power Machine Learning  capable IoT devices.

The QuickFeather board is powered by QuickLogic’s EOS™ S3, the first FPGA-enabled SoC to be fully supported in the Zephyr RTOS, with flexible eFPGA logic integrated with an Arm Cortex®-M4F MCU and functionality such as:

Andon is a methodology originally designed by Toyota in the 80’s to allow responding faster and more efficiently to issues on manufacturing lines.

Skoda is modernizing the methodology to allow operators to respond more quickly to production problems, as well as automatically notify supervisors, leveraging Azure IoT Hub and Office 365.

Stepan Bechynsky joins us on the IoT Show to show how the solution implemented at Skoda works using the “hello world” of IoT demos: a simple button that operators can press to signal problems on a piece of equipment and that integrates into business applications such as Microsoft Teams.

You can learn more about Andon at https://aka.ms/iotshow/andon

As developers, we have the ability to use machine learning to our applications to create very unique experiences and solve difficult problems for our customers.

In this video, Martina and Bruno Capuano sit with Scott to show us how we can use Custom Vision and IoT devices to recognize objects in the real world.

This is a great demo of how to get started with ML without having to train a model, and also how to get our children interested in technology.

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Do you need AI video smarts on the edge?

Then, SolidRun, a developer and manufacturer of high-performance edge computing hardware, and application-specific integrated circuit (ASIC) chip manufacturer Gyrfalcon Technology has a server for you:

The Arm-based, Linux-powered Janux GS31 AI inference server.

What’s an AI inference server you ask? Once you’ve trained a neural network with machine learning to recognize, say, cars and spaces, it’s learned lessons can be built into an application. That program can then infer things about new data based on its training. So, for example, an AI-empowered traffic cop might infer when someone’s speeding or has run a red light.