Edgify_logo

Edgify

Collaborative yet distributed learning for ML and DL models directly on Edge devices.

Business Overview

Active
Operating Status
Sep 2014
Founded
21
Employees
B2B
Business Model
Software
Offering Type
Description
Train Deep Learning Models Directly on the Edge!Edgifys framework gives any edge unit the ability to run Deep learning and AI training locally, without the need to extract the data to a server or having to pay the network costs of Model building. We call this self training AI.By training AI models at the edge where the data is generated, one can train on the entire data generated, without compressing, or sampling the data in order to make it easily transferable to the cloud/server, and without the need to anonymise it for privacy reasons. This ability to decentralise the training process, and by doing so, allowing to train on entire datasets, will bring forth a potential new level of accuracy to the world of AI. These new obtainable accuracies can generate both high financial rewards in some verticals (retail, intelligent homes, manufacturing), and life-saving solutions in others (autonomous cars, medical).
Business Stage
Launched
Revenue Stage
Revenue Generating
Sectors

Board Members and Advisors

Partner and Board Observer

Investors

Investor

Investor Type

Series Preference

Pre-Seed, Series A
Seed, Series A, Series B, Series C, Series G

Funding Rounds

$24,000,000
Total Funding

6

Funding Rounds
Seed
Last Funding Series

Date Announced

Funding Round

Amount Raised

Investors

AI Technology Stack™

AI Team Size
4
AI Technologies
AI Description
AI Technologies
AI Algorithms
Frameworks and Libraries
Coding Languages
AI Inference
AI Cloud Provider

Research Publications

April 2021
Label noise presents a real challenge for supervised learning algorithms. Consequently, mitigating label noise has attracted immense research in recent years. Noise robust losses is one of the more promising approaches for dealing with label noise, as these methods only require changing the loss ...
October 2019
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling ...

In The News

coverstartuphubai

Edge computing startup Edgify secures $6.5M Seed from Octopus, Mangrove and semiconductor | TechCrunch

October 13, 2020

Israeli startup Edgify raises $6.5M to run distributed ML models at the supermarket

Israeli startup Edgify raises $6.5M to run distributed ML models at the supermarket | Geektime

October 12, 2020

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