Amazon SageMaker Edge Manager now integrates with AWS IoT Greengrass to simplify deployment to fleets of edge devices
Amazon SageMaker Edge Manager allows you to optimize, secure, monitor, and maintain machine learning (ML) models on fleets of smart cameras, robots, personal computers, and mobile devices. With SageMaker Edge Manager, you can use your own model deployment mechanism to bring model packages to edge devices. Starting today, SageMaker Edge Manager also integrates with AWS IoT Greengrass Version 2, allowing you to deploy your ML model packages and Edge Manager agent (the on-device inference engine of Edge Manager) to fleets of edge devices easily.
Amazon SageMaker Edge Manager allows you to optimize, secure, monitor, and maintain machine learning (ML) models on fleets of smart cameras, robots, personal computers, and mobile devices. With SageMaker Edge Manager, you can use your own model deployment mechanism to bring model packages to edge devices. Starting today, SageMaker Edge Manager also integrates with AWS IoT Greengrass Version 2, allowing you to deploy your ML model packages and Edge Manager agent (the on-device inference engine of Edge Manager) to fleets of edge devices easily.
Prior to this launch, the only way for you to deploy the Edge Manager agent to your devices was to manually copy the Edge Manager agent to set up your devices and fleets for Edge Manager. If you already had an AWS IoT Greengrass device, deploying your model package to the device using AWS IoT Greengrass would have required you to custom build a component from scratch and manually associate your AWS IoT IAM role with the role alias generated by SageMaker Edge Manager.
With the integration of SageMaker Edge Manager and AWS IoT Greengrass V2, you can now get the Edge Manager agent as an AWS Greengrass V2 component. AWS IoT Greengrass components are software modules such as app building blocks, libraries, or any code that will run on an AWS IoT Greengrass device. You can use a pre-built public Edge Manager component which is maintained by AWS, or use a private Edge Manager component which can be auto generated when you package your machine learning model with the CreateEdgePackagingJob API. You can also create components for your edge application and inference logic, and manage the deployment of these components from AWS IoT console to your devices. SageMaker Edge Manager also allows you to reuse your existing AWS IoT role alias. If you do not have one yet, Edge Manager will generate a role alias as part of the Edge Manager packaging job. You no longer need to associate a role alias generated from the SageMaker Edge Manager packaging job with your AWS IoT Role.
You can now use Amazon SageMaker Edge Manager with the integrated deployment capability in the following AWS regions: US East (N. Virginia), US East (Ohio), US West (Oregon), EU (Ireland), EU (Frankfurt), and Asia Pacific (Tokyo). To learn more and get started, visit the Amazon SageMaker Edge Manager documentation.