Add ML to Analyze and visualize your IoT data

Author: asergaz

articles/iot/iot-overview-analyze-visualize.md

@@ -15,7 +15,7 @@ ms.custom: template-overview
 
 This overview introduces the key concepts around the options to analyze and visualize your IoT data. Each section includes links to content that provides further detail and guidance.
 
-In Azure IoT, analysis and visualization services are used to identify and display business insights derived from your IoT data. For example, you can use a machine learning model to analyze device telemetry and predict when maintenance should be carried out on an industrial asset. You can also use a visualization tool to display a map of the location of your devices.
+In Azure IoT, analysis and visualization services are used to identify and display business insights derived from your IoT data. For example, you can use a machine learning model to analyze device sensor data and predict when maintenance should be carried out on an industrial asset. You can also use a visualization tool to display a map of the location of your devices.
 
 # [Edge-based solution](#tab/edge)
 
@@ -55,6 +55,14 @@ The following articles show some examples of how to use Microsoft Fabric to anal
 - [Tutorial: Get insights from your processed data](../iot-operations/end-to-end-tutorials/tutorial-get-insights.md)
 - [Tutorial: Send data from an OPC UA server to Azure Data Lake Storage Gen 2](../iot-operations/connect-to-cloud/tutorial-opc-ua-to-data-lake.md)
 
+### Azure Machine Learning
+
+[Azure Machine Learning](/azure/machine-learning/overview-what-is-azure-machine-learning) is a cloud-based service for building, training, and deploying machine learning models. It provides a variety of tools and services to help you create and manage machine learning workflows. You can use Azure Machine Learning to analyze IoT data and build predictive models. On Azure Arc-enabled Kubernetes clusters, such as Azure IoT Operations, you can train and deploy machine learning models at the edge with the [Kubernetes compute target in Azure Machine Learning](/azure/machine-learning/how-to-attach-kubernetes-anywhere).
+
+- [Deploy Azure Machine Learning extension on Arc-enabled Kubernetes cluster](/azure/machine-learning/how-to-deploy-kubernetes-extension)
+- [Enable machine learning inference on an Azure IoT Edge device](/azure/architecture/guide/iot/machine-learning-inference-iot-edge)
+
+
 ### Azure Data Explorer
 
 [Azure Data Explorer](/azure/data-explorer/data-explorer-overview/) is a fully managed, high-performance, big-data analytics platform that makes it easy to analyze high volumes of data in near real time. The following articles and tutorials show some examples of how to use Azure Data Explorer to analyze and visualize IoT data:

articles/iot/iot-overview-device-connectivity.md

@@ -207,7 +207,7 @@ Azure IoT Operations is an edge runtime environment that hosts the services to c
 
 Data flows provide data transformation and data contextualization capabilities before routing messages to various locations including cloud endpoints.
 
-Azure IoT Operations runs on Azure Arc-enabled edge Kubernetes clusters, enabling a fully automated machine learning operations in hybrid mode, including training and AI model deployment steps that transition seamlessly between cloud and edge. To learn more, see [Introduction to Kubernetes compute target in Azure Machine Learning](/azure/machine-learning/how-to-attach-kubernetes-anywhere).
+Azure IoT Operations runs on Azure Arc-enabled Kubernetes clusters, enabling a fully automated machine learning operations in hybrid mode, including training and AI model deployment steps that transition seamlessly between cloud and edge. To learn more, see [Introduction to Kubernetes compute target in Azure Machine Learning](/azure/machine-learning/how-to-attach-kubernetes-anywhere).
 
 ### [Cloud-based solution](#tab/cloud)