Real-time AI Processing at the Edge
Organizations today are looking for streamlined and more efficient ways to accelerate ML-enabled processing for their edge devices.
Organizations today are looking for streamlined and more efficient ways to accelerate ML-enabled processing for their edge devices.
Real-time AI processing at the edge refers to the use of artificial intelligence (AI) algorithms to analyze and process data in real time, directly at the source of the data rather than in a remote centralized location. However, challenges persist with reducing large model architectures to run on smaller devices, ensuring security, bridging skills gaps, and enabling orchestration back to a centralized solution for monitoring and retraining as needed.
Organizations today are looking for streamlined and more efficient ways to accelerate ML-enabled processing for data collected on their edge devices. There are several benefits to this approach:
Watch this video focused on AI-powered processing at the edge and learn how you can get started running AI models on thousands of devices from a central location using Modzy Edge. We demonstrate how you can run a computer vision model on an NVIDIA Jetson Nano to process video in real-time, and share an example of an ML model analyzing data from atmospheric sensors.