Challenges in deploying Edge AI applications Introduction AI/ML use-cases are pervasive. The enterprise use-cases can be broadly categorized based on the three core technical capabilities enabling them: Predictive Analytics, Computer Vision (CV) and Natural Language Processing (NLP). The Enterprise AI story has so far been focused on the Cloud. The general perception is that it … Read more
Is synthetic data more privacy compliant? Federated learning [1], also known as Collaborative Learning, or Privacy preserving Machine Learning, enables multiple entities who do not trust each other (fully), to collaborate in training a Machine Learning (ML) model on their combined dataset; without actually sharing data — addressing critical issues such as privacy, access rights … Read more
Abstract This article compares a couple of common Edge AI platforms in terms of hardware, performance, price and development environment. Edge AI is a very exciting field today, with a lot of development and innovations coming. For years, there has been a clear tendency for machine learning prediction to move down to embedded hardware that … Read more
Deploying AI/ML models on Edge Devices Debmalya Biswas Mar 25 · 11 min read (Darwin Edge, Switzerland) Miljan Vuletić, Vladimir Mujagić, Marko Atanasievski, Nikola Milojević, Debmalya Biswas Abstract. Edge AI enables intelligent solutions to be deployed on edge devices, reducing latency, allowing offline execution, and providing strong privacy guarantees. Unfortunately, achieving efficient and accurate execution … Read more
