Navigating the intelligent edge: answers to top questions
by Olivier Bloch on 23rd September 2019 at 13:00
Over the past ten years, Microsoft has seen embedded IoT devices get progressively smarter and more connected, running software intelligence near the point where the data is being generated within a network. And having memory and compute capabilities at the intelligent edge solves multiple conundrums related to connectivity, bandwidth, latencies, and privacy/security.
Expanded Azure Maps coverage, preview of Azure Maps feedback site, and more
by Outi Nyman on 12th September 2019 at 14:46
Azure Maps services continue to expand our support for Microsoft enterprise customers’ needs in Azure. And, we’ve been busy expanding our capabilities.
Five best practices for unlocking IoT value
by Tony Shakib on 11th September 2019 at 13:00
Accenture and Avanade won the 2019 Microsoft Internet of Things Partner of the Year award this past spring. At the Microsoft Inspire partner conference in July, Brendan Mislin, Managing Director, Industry X.0 IoT Lead at Accenture, shared some insights and best practices that have helped this award-winning partner unlock the value of Azure IoT for our mutual customers.
Microsoft’s connected vehicle platform presence at IAA, the Frankfurt Auto Show
by Tara Prakriya on 8th September 2019 at 22:00
A connected vehicle solution must enable a fleet of potentially millions of vehicles, distributed around the world, to deliver intuitive experiences including infotainment, entertainment, productivity, driver safety, driver assistance. In addition to these services in the vehicle, a connected vehicle solution is critical for fleet solutions like ride- and car-sharing as well as phone apps that incorporate the context of the user and the journey.
Microsoft and Qualcomm accelerate AI with Vision AI Developer Kit
by Anne Yang on 3rd September 2019 at 13:00
Artificial intelligence (AI) workloads include megabytes of data and potentially billions of calculations. With advancements in hardware, it is now possible to run time-sensitive AI workloads on the edge while also sending outputs to the cloud for downstream applications.