Machine Learning (ML) technology is the heart of differentiated IoT devices and defines what makes a device smart. Maintaining, monitoring and updating IoT devices is essential to ensure that these smart capabilities continue to deliver the value promised throughout the lifecycle of the device. This requires an MLOps strategy tailored for IoT scale.

This presentation will show how to build a model once, deploy it to a fleet, monitor and maintain that model using cloud technologies.

Demo and example code: https://github.com/aws-samples/greengrass-v2-sagemaker-edge-manager-python