Exploring Edge AI in Cloud Computing Solutions

[charliekthrn]

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Hi everyone,

I’ve recently started delving into Edge AI and its applications, and I’m intrigued by its role in cloud computing environments. While browsing GitHub, I came across repositories focused on Edge AI, including curated lists of tools, platforms, and inference engines optimized for edge devices. For instance, I saw mentions of NVIDIA Jetson boards and Google's Coral Dev Boards as hardware options for deploying AI models directly on devices instead of relying solely on the cloud.

This brings me to my question: how do cloud solutions integrate with Edge AI for tasks like real-time data processing or model inference? Specifically, are there best practices or recommended platforms to seamlessly combine edge deployments with centralized cloud management? For example, how would you use something like NVIDIA's TensorRT on an edge device while leveraging cloud resources for heavier AI training tasks?

Additionally, for those experienced in Edge AI, how do you balance latency, security, and cost when deciding what to process locally versus what to send to the cloud? I’d appreciate insights, especially from anyone who’s used tools like TensorFlow Lite, ONNX, or platforms such as Azure IoT Edge.

Looking forward to your advice and experiences. Also, if there are repositories on GitHub or similar resources that you think would be helpful, please share them!

Thanks in advance!

[mastertanzeel]

Hi @charliekthrn ,

Edge AI is an exciting field, and your questions touch on some key aspects of its integration with cloud computing. Generally, the edge handles tasks that need low latency or local decision-making, such as real-time data processing, while the cloud is used for more computationally intensive tasks like AI model training or large-scale data analysis. For example, you can use NVIDIA TensorRT on edge devices for optimized model inference, and tools like Azure IoT Edge can act as a bridge, enabling seamless communication between the edge and cloud. Best practices often include designing models that are lightweight enough for edge devices while ensuring compatibility with the cloud for updates and monitoring.

Balancing latency, security, and cost usually depends on the specific use case. For real-time or sensitive data, processing locally on edge devices is preferred to reduce latency and enhance privacy. On the other hand, less time-sensitive tasks or those requiring more power can be offloaded to the cloud. Tools like TensorFlow Lite and ONNX are great for running models on edge devices, while platforms like AWS Greengrass or Azure IoT Edge can help manage cloud-edge integration effectively. If you're looking for GitHub repositories, I'd recommend starting with curated lists of Edge AI tools or repositories focused on TensorRT and Azure IoT Edge for real-world examples.

Hope this helps!