{"id":1868,"date":"2022-03-01T17:50:05","date_gmt":"2022-03-01T17:50:05","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/01\/anomaly-detection-with-amazon-sagemaker-edge-manager-using-aws-iot-greengrass-v2\/"},"modified":"2022-03-01T17:50:05","modified_gmt":"2022-03-01T17:50:05","slug":"anomaly-detection-with-amazon-sagemaker-edge-manager-using-aws-iot-greengrass-v2","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/03\/01\/anomaly-detection-with-amazon-sagemaker-edge-manager-using-aws-iot-greengrass-v2\/","title":{"rendered":"Anomaly detection with Amazon SageMaker Edge Manager using AWS IoT Greengrass V2"},"content":{"rendered":"
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Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. 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.<\/p>\n

Another 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 includes installing applications, deploying application updates, deploying new configurations, monitoring device performance, troubleshooting devices, authenticating and authorizing devices, and securing the data transmission. 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.<\/p>\n

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<\/a>, or build custom applications that rely on services such as AWS IoT Core<\/a>. Although it can provide the necessary scalability and reliability, building such custom solutions comes at the cost of additional maintenance and requires specialized skills.<\/p>\n

Amazon SageMaker<\/a>, together with AWS IoT Greengrass<\/a>, can help you overcome these challenges.<\/p>\n

SageMaker provides Amazon SageMaker Neo<\/a>, 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. As devices proliferate, you may have thousands of deployed models running across your fleets. Amazon SageMaker Edge Manager<\/a> allows you to optimize, secure, monitor, and maintain ML models on fleets of smart cameras, robots, personal computers, and mobile devices.<\/p>\n

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<\/a>. It takes inspiration from Monitor and Manage Anomaly Detection Models on a fleet of Wind Turbines with Amazon SageMaker Edge Manager<\/a> by introducing AWS IoT Greengrass for deploying and managing inference application and the model on the edge devices.<\/p>\n

In the previous post, the author used custom code relying on AWS IoT services, such as AWS IoT Core<\/a> and AWS IoT Device Management<\/a>, to provide the remote management capabilities to the fleet of devices. Although that is a valid approach, developers need to spend a lot of time and effort to implement and maintain such solutions, which they could spend on solving the business problem of providing efficient, performant, and accurate anomaly detection logic for the wind turbines.<\/p>\n

The previous post also used a real 3D printed mini wind turbine and Jetson Nano<\/a> to act as the edge device running the application. Here, we use virtual wind turbines that run as Python threads within a SageMaker notebook. Also, instead of Jetson Nano, we use Amazon Elastic Compute Cloud<\/a> (Amazon EC2) instances to act as edge devices, running AWS IoT Greengrass software and the application. We also run a simulator to generate measurements for the wind turbines, which are sent to the edge devices using MQTT<\/a>. We also use the simulator for visualizations and stopping or starting the turbines.<\/p>\n

The previous post goes more in detail about the ML aspects of the solution, such as how to build and train the model, which we don\u2019t cover here. We focus primarily on the integration of Edge Manager and AWS IoT Greengrass V2.<\/p>\n

Before we go any further, let\u2019s review what AWS IoT Greengrass is and the benefits of using it with Edge Manager.<\/p>\n

What is AWS IoT Greengrass V2?<\/h2>\n

AWS IoT Greengrass is an Internet of Things (IoT) open-source edge runtime and cloud service that helps build, deploy, and manage device software. You can use AWS IoT Greengrass for your IoT applications on millions of devices in homes, factories, vehicles, and businesses. AWS IoT Greengrass V2 offers an open-source edge runtime, improved modularity, new local development tools, and improved fleet deployment features. It provides a component framework that manages dependencies, and allows you to reduce the size of deployments because you can choose to only deploy the components required for the application.<\/p>\n

Let\u2019s go through some of the concepts of AWS IoT Greengrass to understand how it works:<\/p>\n