![]() In the previous post, the author used custom code relying on AWS IoT services, such as AWS IoT Core and AWS IoT Device Management, to provide the remote management capabilities to the fleet of devices. It takes inspiration from Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager by introducing AWS IoT Greengrass for deploying and managing inference application and the model on the edge devices. This post shows how to train and deploy an anomaly detection ML model to a simulated fleet of wind turbines at the edge using features of SageMaker and AWS IoT Greengrass V2. Amazon SageMaker Edge Manager allows you to optimize, secure, monitor, and maintain ML models on fleets of smart cameras, robots, personal computers, and mobile devices. As devices proliferate, you may have thousands of deployed models running across your fleets. SageMaker provides Amazon SageMaker Neo, which is the easiest way to optimize ML models for edge devices, enabling you to train ML models one time in the cloud and run them on any device. Although it can provide the necessary scalability and reliability, building such custom solutions comes at the cost of additional maintenance and requires specialized skills.Īmazon SageMaker, together with AWS IoT Greengrass, can help you overcome these challenges. On a smaller scale, you can adopt solutions such as manually logging in to each device to run scripts, use automated solutions such as Ansible, or build custom applications that rely on services such as AWS IoT Core. These are foundational features for any edge application, but creating the infrastructure needed to achieve a secure and scalable solution requires a lot of effort and time. This includes installing applications, deploying application updates, deploying new configurations, monitoring device performance, troubleshooting devices, authenticating and authorizing devices, and securing the data transmission. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available.Īnother common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale. This makes deploying and managing these models more complex. This is primarily due to the hardware, software, and networking restrictions at the edge sites. Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud.
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