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February 2, 2020 at 5:34 am #39576
#News(IoTStack) [ via IoTGroup ]
Headings…
Artificial Intelligence on Edge devices: an engineering led approach
Engineering Methodology
Implications for AI and Edge
CICD for Edge devices
Conclusion
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Artificial Intelligence – Cloud and Edge implementations takes an engineering-led approach for the deployment of AI to Edge devices within the framework of the cloud.
Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and buildings.
But when we consider the deployment of AI to Edge devices, we consider an interdisciplinary engineering approach.
AI on Edge devices could include many areas like Drones, Edge analytics, embedded FPGA etc.
If multiple solutions exist, engineers weigh each design choice based on their merit and choose the solution that best matches the requirements.
Engineers identify, understand, and interpret the constraints on a design in order to yield a successful result.
By understanding the constraints, engineers derive specifications for the limits within which a viable object or system may be produced and operated.
Looking back at the definition of Engineering above, we can infer some key themes which apply for deployment of AI and Edge computing in the cloud:
Solve business problems i.e. understanding business problems and using AI, Edge and Cloud to improve a business process (Digital transformation)
The model is trained in the cloud and deployed on the edge
The model is deployed on the edge and the edge device provides a feedback loop to improve the business process.
The telemetry function captures data from edge devices and stores it in a data based in the cloud.
The models could be trained and deployed to edge devices.
CICD (Continious integration – continuous deployment) is a logical extension to containers on edge devices
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