This code sample provides an end-to-end solution that manages the lifecycle of ML models deployed to a simulated wind turbine fleet. We'll use Amazon SageMaker to prepare a Machine Learning (ML) model and AWS IoT services, both AWS IoT Jobs and AWS IoT Greengrass v2 to deploy and run this model to an edge device (Raspberry Pi). To be agnostic of the different device frameworks and ML accelerator we leverage ONNX Runtime.
Using a Machine Learning model (Autoencoder) you can analyze the turbine sensors data and detect anomalies. This technique is important to improve the maintenance process and reduce the operational cost.
This code sample is a variant of the solution presented in this blog post, without using Sagemaker Edge Manager. The blog post contains useful information about the custom ML model trained.
Two options are available to deploy this code sample:
- deploy using AWS IoT Jobs: once a specific model version is approved by an IIoT Engineer in the Amazon Sagemaker model registry, a build step exports the ML model to the ONNX format, and an AWS Lambda function creates an AWS IoT job targetting all devices.
- deploy using AWS IoT Greengrass v2: once a specific model version is approved by an IIoT Engineer in the Amazon Sagemaker model registry, a build step creates three AWS IoT Greengrass components, and an AWS Lambda function creates a Greengrass deployment targetting all Greengrass core devices.
Chose one of the option above and follow the dedicated steps as desribed below
Documentation is located here
Documentation is located here
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