The Internet of Things (IoT) is arriving at pace, enabling new applications and business models across many consumer, enterprise, and industrial sectors. When you attach a "thing" to the Internet, you connect new data sources, which can be costly and resource-intensive to store or upload. As IoT continues to grow, organizations and users alike are finding immense benefit in applying IoT paired with Machine Learning and Azure Sphere. ML is excellent at processing noisy and complex sensor data to determine higher-level insights like "operating normally" versus "cooling unit failed" in a vibration sensor data stream, or "wake word detected" in an audio stream, or "items placed correctly" versus "item out of place" in a video stream. ML can be used on the IoT device itself, in the cloud, or a combination of both.

Cartesiam Award-winning technology NanoEdge AI Studio is now fully available on Microsoft Azure Sphere. It allows developer, without data-science skills and complex dataset, to easily create a dynamic machine learning library to be integrated into the main program running on any arm cortex M microcontroller. Learning and inference at the Edge has never been so simple. Try it out for free and download the trial version of your choice!

Microsoft CEO Satya Nadella imagines a world with an ever-expanding set of connected devices that process data locally and work in tandem with the cloud – and his company has designed its entire multibillion-dollar cloud business around that concept. Nadella revealed the company's strategy for edge computing during Microsoft's recent shareholders meeting. Edge computing is a buzzword, but it basically means processing data on the devices themselves, instead of offsite in the cloud. Think of a self-driving car. It needs to be able to process data and make split-second decisions without the delays that would come if that data had to be processed far away in the cloud.

Spektacom, a sports tech startup founded by Anil Kumble, one of the most accomplished cricketers in India, partnered with Microsoft to bring cutting-edge technology to the game of cricket. Spektacom built a platform that includes a 5-gram sticker that attaches itself to the cricket bat, a stump box that acts as an IoT gateway, and AI-powered analytics to deliver insights on the batting style of a batsman. The data collected in the cloud is instantly run through a machine learning model that assesses the quality of a shot. Anil officially calls the IoT-enabled bat as a power bat, which doesn't deviate from the specifications of a standard cricket bat. The technology behind the power bat is fascinating.

Qualcomm's new Vision Intelligence Platform is part of this trend. The Qualcomm platform features the chipmaker's first family of system-on-chips (SoCs) built specifically for the IoT using an advanced 10 nm FinFET (Fin Field Effect Transistor) process that produces substantially faster performance and efficiencies than previous chips. The platform's chip models, QCS605 and QCS603, are designed to provide greater computing power for on-device cameras and machine learning. The company envisions these chips being used for a variety of applications, including robots, the smart home, and smart cameras. For utilities, these could become valuable in cameras mounted amid critical infrastructure such as substations, or in drones that monitor the grid.