Edge Computing
エッジコンピューティング
We use around 10 intelligent objects everyday, but we will quadruple this number in the coming years.
The amount of objects connected to the network will increase. From logistics solutions like monitoring shelves in warehouses to optimize the arrangement of goods, thus reducing staff workload and enhancing efficiency, to smart cities that monitor traffic, alleviating congestion and optimizing public transport schemes.
New technologies such as 5G make any sensor suitable to be connected to the network. Soon, we will see how the number of nodes that manages the connection will increase. That is where Edge Computing comes into play. The massive growth of information will revolutionize the way to manage it.
Moving the application to the far edge
Decreasing power consumption, network
usage and latency
Datacenter
Computers
Near edge
Computer at point of presence
Traditional edge
Rugged computer
Far edge
End device
Edge Computing
Challenges
Manage billions of data within the fraction of a second
The massive increase of information that has to be processed by Artificial Intelligence algorithms, will be difficult to manage through the cloud affecting its capacity and latency.
Compliance
On the other hand, the more devices are connected to the network, the greater the volume of private information is circulating. However, thanks to Edge Computing, we can minimize the number of private information flowing on the net complying with the protection laws provided different regulations.
Network Overload
Many devices connected at the same time can generate an amount of traffic that can overload the system.
Applications that rely on split-second decisions are affected by network latency.
Security Problems
We want all devices to work, but one error in the cloud can disable the whole network of devices connected to that server. Everything could stop working!
Network Reliability
Many devices connected at the same time can generate an amount of traffic that can overload the system.
The Value of Edge
Computing
Working with Sensing Applications for Computer Vision
Edge Computing allows to optimize sensor technology, like correcting automatically the position of the camera lenses and identifying patterns within a few seconds.
Adaptive Solutions: Continuously Tune and Update AI Models
Imagine an algorithm capable of constantly adapting itself to the changing environment. Edge Computing is increasing the device autonomy, allowing it to make decisions without having to depend on the server.
We Offer Companies and Service Providers to Bet on the Cloud Hyperservice
Guaranteeing an exceptional experience for their customers: from shorter loading time to smart web forms. The introduction to Edge Computing can create a unique brand experience.
Federated Learning
Machine learning requires a data center where all the information is managed. We are taking one step further. Federated learning allows Artificial Intelligence to learn in a collaborative way, keeping the training data in the device and separating machine learning from data storage in the cloud.