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:
Reduced Latency: By processing data at the edge, you can reduce the time it takes to analyze and act on data, as the data does not have to be transmitted over a network to a central location for processing. This is particularly important in applications where quick response is crucial, such as in self-driving cars or industrial automation.
Improved Reliability: By processing data at the edge, you can improve the reliability of the system as a whole. For example, if a centralized server is used for processing, a network outage or server failure could prevent the system from functioning. With edge processing, the system can continue to operate even if the connection to the central server is lost.
Enhanced Security: Processing data at the edge can also improve security, as sensitive data does not have to be transmitted over a network where it could potentially be intercepted.
Reduced Bandwidth Requirements: By processing data at the edge, you can also reduce the amount of data that needs to be transmitted over a network, which can help to reduce bandwidth requirements and lower costs.
Greater Scalability: Edge processing can also make it easier to scale a system, as the centralized server does not have to process all of the data. This can help to reduce the burden on the central server and make it easier to handle large volumes of data.
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.
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