Securely connect edge devices to the cloud, enable software and firmware updates, and manage the exchange of data with Cloud IoT Core.
Run ML inferences of pre-trained TensorFlow Lite models locally, significantly increasing the processing power and versatility of edge devices. This enables the next wave of machine learning applications and use cases.
How Cloud IoT Edge works
Deploy ML models at the edge for faster real-time prediction
Make real-time predictions for mission-critical IoT applications. Edge ML, built on a TensorFlow Lite runtime, uses CPUs and hardware accelerators like Edge TPUs and GPUs to run on-device machine learning models that provide faster predictions for critical IoT applications than general-purpose IoT gateways — all while ensuring data privacy and confidentiality. Plus, Edge ML and Edge TPU have been extensively tested to natively run open source benchmark models like MobileNets and Inception V3.
Enhance operational reliability
Locally store, process, and derive intelligence from data at the edge, while seamlessly interoperating with the rest of the Cloud IoT platform. This allows you to build robust, enterprise-ready IoT solutions on premises, without having to worry about intermittent cloud connectivity. This can be critical for video and audio applications that require real-time processing and for serving offline use cases where devices can’t reliably connect to a network.
Secure your devices and your data
Securely connect your globally distributed devices on Google Cloud IoT platform. Cloud IoT Edge processes and analyzes images, videos, gestures, acoustics, and motion locally on edge devices instead of sending raw data to the cloud. This edge processing obviates the need to send large and potentially sensitive data streams to the cloud. Additionally, Cloud IoT Edge uses JSON Web Token to authenticate edge devices with Google Cloud instead of relying on the mutual authentication of a TLS stack.
Cost-effective at any scale
Optimize the integration cost of your IoT solution with a comprehensive software and hardware stack from Google Cloud IoT. This integrated stack uses Cloud IoT Edge, Linux OS, and Edge TPU to transform ordinary edge devices into smart and powerful gateways.
Industry use cases
Detect anomalies in high-velocity assembly lines that provide early warnings for potential issues, enabling you to get ahead of the problem with predictive maintenance.
Deliver hyper-personalized product recommendations, offers, and communications based on in-store shopper behaviors.
Enable intelligent technologies like collision avoidance, traffic routing, and eyes off the road detection system.
Xee’s connected car platform makes driving simpler, safer, and more economical. Cloud IoT Edge and Edge TPU enables accelerated ML inferences at the edge, allowing the Xee platform to analyze images and radar data faster, detect potential driving hazards such as road conditions and tire wear and tear, and to alert drivers with real-time precision.Romain Crunelle, Chief Technical Officer, Xee
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Cloud IoT Edge Alpha
Apply for early access to the Cloud IoT Edge and Edge TPU development board.