A fundamental shift in AI Training
A Distributed yet Collaborative Framework for training DL and ML at the Edge
We allow any company, from any industry, to train complete DL and ML models, directly on their own edge devices. Reaching close to perfect accuracy, without the need to transfer any of the data to the cloud.
When we say edge we mean...
The Reasons for Training your AI at the Edge
Privacy by Design
Training AI at the edge is the ultimate way to ensure data privacy and security, as no information ever leaves the device. Ever!
Scalable by Definition
Our platform scales inherently with our clients. The more devices you have, the more processing power you have and there is never a need to invest in new infrastructure.
Zero Upload Time/Cost
ML and DL are data heavy by definition. Uploading the data generated by the edge devices in order to train on it can be either a strain on the Broadband or simply impossible. Training on the devices reduces the amount shared by several orders of magnitude and completely eliminates the costs associated with data transfers, storage and training on server.
The accuracy of your AI models is directly related to the data you train on. The amount of data you train on, the quality of the data you train on, and how close is the data you train on to the data you will need to infer on. By training at the edge, you can train on all your data, you never have to compress it or harm it in any way, and it is literally the same data you will be inferring on!
Our unique IP and process
The Edgify Edge Training Loop
We developed the ability to run computationally intensive processes on edge devices with limited CPU and GPU, including solutions for low connectivity, asynchronistic data, and data split into unequal batches. Turn any edge device into a DL training machine! This can be done on any device that is connected and has CPU / GPU such as simple cameras, Cashiers, connected cars, printers, laptops, mobile phones, etc.
The Edgify Collaborative Controller
In order to overcome the distributed nature of training at the edge (mainly the fact that not all edge devices see the same data and therefore have different levels of models) we have developed a collaborative controller that aggregates all the different models generated by the edge devices of the same client, and optimises them into one master model which is then shared back to the edge devices.
Upload and control your Edge Devices
Track your different Edges’ contribution to the model
Inspect and Evaluate your Model and see the accuracy increasing ongoingly
Deploy your model across your entire edge network
The Edgify Framework is vertical agnostic. As long as you have edge devices, and are working on integrating AI into your workflow, there is a use case for using the Edgify platform instead of servers / clouds.
We provide retailers frictionless, computer vision based SCO and autonomous store framework with no infrastructure costs, fast and easy deployment from store to store, the ability to add new items to their detection models at 80% less of the cost and at over 99% accuracy!