Watch this video for a deep dive on the benefits of running AI models at the edge.
Running AI models at the edge has a number of benefits.
Reduced latency: By running AI models at the edge, the time it takes to process and return a result is significantly reduced, as the data does not need to be transmitted to a remote server and back. This is especially beneficial in applications where low latency is critical, such as in real-time decision making or in autonomous systems.
Improved privacy and security: When AI models are run at the edge, sensitive data does not need to be transmitted to a remote server, which can improve privacy and security. This is important in cases where data privacy regulations are strict or when the data being processed is sensitive or confidential.
Improved reliability: Running AI models at the edge can improve the reliability of the system, as it reduces the dependency on a remote server and the need for a stable internet connection. This is particularly important in cases where the internet connection may be unreliable or when the system needs to operate in areas with limited or no connectivity.
Increased efficiency: By running AI models at the edge, data can be processed closer to the source, which reduces the amount of data that needs to be transmitted and stored. This can improve the efficiency of the system and reduce the cost of data storage and transmission.
Increased flexibility: Running AI models at the edge allows for greater flexibility in terms of deployment, as the models can be deployed directly on devices rather than relying on a central server. This allows for the deployment of AI models in a wider range of environments and scenarios.
Running AI models at the edge - tech talk
This tech talk walks through a new method to deploying, running, and securing AI models at the edge that allows for faster processing, reduced latency, and increased security.
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