Monday, April 15, 2019

AWS IOT Greengrass ML inference

Right now am working one of the use case using AWS Greengrass.

AWS Greengrass is a service that allows you to take a lot of the capabilities provided by the AWS IoT service and run that at the edge closer to your devices. AWS Greengrass ensures your IoT devices can respond quickly to local events, use Lambda functions running on Greengrass Core to interact with local resources, operate with irregular connections, stay updated with over the air updates, and minimize the cost of transmitting IoT data to the cloud.
Deep Learning challenges at the Edge
Resource-constrained devices
CPU, memory, storage, power consumption.
Network connectivity
Latency, bandwidth, availability.
On-device prediction may be the only option.
Deployment
Updating code and models on a fleet of devices is not easy.
Value of ML inference at the Edge
·       Latency
·       Bandwidth
·       Availability
·       Privacy
https://aws.amazon.com/solutions/case-studies/iot/
https://aws.amazon.com/greengrass/faqs/

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