There are also many benefits to be gained from intelligent, real-time decision-making at the point where these devices connect to the network—what’s known as the “edge.” Manufacturing companies can detect anomalies in high-velocity assembly lines in real time. Retailers can receive alerts as soon as a shelved item is out of stock. Automotive companies can increase safety through intelligent technologies like collision avoidance, traffic routing, and eyes-off-the-road detection systems.
But real-time decision-making in IoT systems is still challenging due to cost, form factor limitations, latency, power consumption, and other considerations. We want to change that.
Bringing machine learning to the edge
Today, we’re announcing two new products aimed at helping customers develop and deploy intelligent connected devices at scale: Edge TPU, a new hardware chip, and Cloud IoT Edge, a software stack that extends Google Cloud’s powerful AI capability to gateways and connected devices. This lets you build and train ML models in the cloud, then run those models on the Cloud IoT Edge device through the power of the Edge TPU hardware accelerator.
Read more at Google Cloud
