Daniel Kaempf

Automated Classification of Facial Recognition Tiles Isidora Stankovic, Nevena Milenkovic, Goran Benak, Miljan Vuletic, Nikola Milojevic, Debmalya Biswas — Darwin Edge, Switzerland Abstract. Facial recognition platforms based on Computer Vision/Deep Learning techniques have considerably matured over the last few years. This has led to their sometimes controversial usage in surveillance use-cases. However, it has also … Read more

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