{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Running the fleet of Virtual Wind Turbines and Edge Devices\n", "\n", "**SageMaker Studio Kernel**: Data Science\n", "\n", "After visualizing the data and training/optimizing/packaging the Anomaly detection model, its time to deploy it and test your virtual fleet. In this exercise you will run a local application written in Python3 that simulates 5 Wind Turbines and 5 edge devices. The SageMaker Edge Agent is deployed on the edge devices.\n", "\n", "Here you'll be the **Wind Turbine Farm Operator**. It's possible to visualize the data flowing from the sensors to the ML Model and analyze the anomalies. Also, you'll be able to inject noise (pressing some buttons) in the data to simulate potential anomalies with the equipment.\n", "\n", "
STEP-BY-STEP | \n", "APPLICATION ARCHITECTURE | \n", "
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