Update links

Author: PeterCLu

articles/databox-online/data-box-edge-overview.md

@@ -24,7 +24,7 @@ Azure Stack Edge is a Hardware-as-a-service solution. Microsoft ships you a clou
 Here are the various scenarios where Azure Stack Edge can be used for rapid Machine Learning (ML) inferencing at the edge and preprocessing data before sending it to Azure.
 
 - **Inference with Azure Machine Learning** - With Azure Stack Edge, you can run ML models to get quick results that can be acted on before the data is sent to the cloud. The full data set can optionally be transferred to continue to retrain and improve your ML models. For more information on how to use the Azure ML hardware accelerated models on the Azure Stack Edge device, see 
-[Deploy Azure ML hardware accelerated models on Azure Stack Edge](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-fpga-web-service#deploy-to-a-local-edge-server).
+[Deploy Azure ML hardware accelerated models on Azure Stack Edge](https://docs.microsoft.com/azure/machine-learning/how-to-deploy-fpga-web-service#deploy-to-a-local-edge-server).
 
 - **Preprocess data** - Transform data before sending it to Azure to create a more actionable dataset. Preprocessing can be used to: 
 

articles/databox-online/data-box-edge-technical-specifications-compliance.md

@@ -30,7 +30,7 @@ A Field Programmable Gate Array (FPGA) is included on every Data Box Edge device
 
 | Specification           | Value                  |
 |-------------------------|----------------------------|
-| FPGA   | Intel Arria 10 <br> Available Deep Neural Network (DNN) models are the same as those [supported by cloud FPGA instances](https://docs.microsoft.com/azure/machine-learning/service/how-to-deploy-fpga-web-service#whats-supported-on-azure).| 
+| FPGA   | Intel Arria 10 <br> Available Deep Neural Network (DNN) models are the same as those [supported by cloud FPGA instances](https://docs.microsoft.com/azure/machine-learning/how-to-deploy-fpga-web-service#whats-supported-on-azure).| 
 
 
 ## Power supply unit specifications

articles/iot-edge/tutorial-deploy-machine-learning.md

@@ -15,7 +15,7 @@ ms.custom: mvc
 
 Use Azure Notebooks to develop a machine learning module and deploy it to a Linux device running Azure IoT Edge. 
 
-You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/service/how-to-deploy-to-iot.md).
+You can use IoT Edge modules to deploy code that implements your business logic directly to your IoT Edge devices. This tutorial walks you through deploying an Azure Machine Learning module that predicts when a device fails based on simulated machine temperature data. For more information about Azure Machine Learning on IoT Edge, see [Azure Machine Learning documentation](../machine-learning/how-to-deploy-and-where.md).
 
 The Azure Machine Learning module that you create in this tutorial reads the environmental data generated by your device and labels the messages as anomalous or not.
 
@@ -44,7 +44,7 @@ An Azure IoT Edge device:
 Cloud resources:
 
 * A free or standard-tier [IoT Hub](../iot-hub/iot-hub-create-through-portal.md) in Azure.
-* An Azure Machine Learning workspace. Follow the instructions in [Use the Azure portal to get started with Azure Machine Learning](../machine-learning/service/quickstart-get-started.md) to create one and learn how to use it.
+* An Azure Machine Learning workspace. Follow the instructions in [Use the Azure portal to get started with Azure Machine Learning](../machine-learning/tutorial-1st-experiment-sdk-setup.md) to create one and learn how to use it.
    * Make a note of the workspace name, resource group, and subscription ID. These values are all available on the workspace overview in the Azure portal. You'll use these values later in the tutorial to connect an Azure notebook to your workspace resources. 
 
 

articles/notebooks/use-machine-learning-services-jupyter-notebooks.md

@@ -7,7 +7,7 @@ ms.date: 12/04/2018
 
 # Use Azure Machine Learning in Azure Notebooks Preview
 
-Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning](/azure/machine-learning/service/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios.
+Azure Notebooks comes pre-configured with the necessary environment to work with [Azure Machine Learning](/azure/machine-learning/). You can easily clone a sample project into your Notebooks account to explore a variety of Machine Learning scenarios.
 
 [!INCLUDE [notebooks-status](../../includes/notebooks-status.md)]
 
@@ -34,9 +34,9 @@ Azure Notebooks comes pre-configured with the necessary environment to work with
 
 The Azure Machine Learning documentation contains a variety of other resources that guide you through working with Machine Learning within notebooks:
 
-- [Quickstart: Use Python to get started with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/quickstart-create-workspace-with-python)
-- [Tutorial #1: Train an image classification model with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-train-models-with-aml)
-- [Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)](https://docs.microsoft.com/azure/machine-learning/service/tutorial-deploy-models-with-aml)
-- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/service/tutorial-auto-train-models)
+- [Quickstart: Use Python to get started with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/how-to-configure-environment#local)
+- [Tutorial #1: Train an image classification model with Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/tutorial-train-models-with-aml)
+- [Tutorial #2: Deploy an image classification model in Azure Container Instance (ACI)](https://docs.microsoft.com/azure/machine-learning/tutorial-deploy-models-with-aml)
+- [Tutorial: Train a classification model with automated machine learning in Azure Machine Learning](https://docs.microsoft.com/azure/machine-learning/tutorial-auto-train-models)
 
 Also see the documentation for the [Azure Machine Learning SDK for Python](https://docs.microsoft.com/python/api/overview/azure/ml/intro?view=azure-ml-py).

articles/storage/blobs/data-lake-storage-integrate-with-azure-services.md

@@ -23,7 +23,7 @@ The following table lists the Azure services that support Azure Data Lake Storag
 |Azure Databricks | [Use with Azure Databricks](https://docs.azuredatabricks.net/data/data-sources/azure/azure-datalake-gen2.html) <br> [Quickstart: Analyze data in Azure Data Lake Storage Gen2 by using Azure Databricks](data-lake-storage-quickstart-create-databricks-account.md) <br>[Tutorial: Extract, transform, and load data by using Azure Databricks](https://docs.microsoft.com/azure/azure-databricks/databricks-extract-load-sql-data-warehouse) <br>[Tutorial: Access Data Lake Storage Gen2 data with Azure Databricks using Spark](data-lake-storage-use-databricks-spark.md) |
 |Azure Event Hubs capture| [Capture events through Azure Event Hubs in Azure Blob Storage or Azure Data Lake Storage](https://docs.microsoft.com/azure/event-hubs/event-hubs-capture-overview)|
 |Azure Logic Apps | [Overview - What is Azure Logic Apps?](https://docs.microsoft.com/azure/logic-apps/logic-apps-overview)|
-|Azure Machine Learning|[Access data in Azure storage services](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data)|
+|Azure Machine Learning|[Access data in Azure storage services](https://docs.microsoft.com/azure/machine-learning/how-to-access-data)|
 |Azure Cognitive Search | [Index and search Azure Data Lake Storage Gen2 documents (preview)](https://docs.microsoft.com/azure/search/search-howto-index-azure-data-lake-storage)|
 |Azure Stream Analytics| [Quickstart: Create a Stream Analytics job by using the Azure portal](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-quick-create-portal) <br> [Egress to Azure Data Lake Gen2](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-define-outputs#blob-storage-and-azure-data-lake-gen2) |
 |Data Box|  [Use Azure Data Box to migrate data from an on-premises HDFS store to Azure Storage](data-lake-storage-migrate-on-premises-hdfs-cluster.md)|

includes/designer-notice.md

@@ -11,4 +11,4 @@ ms.date: 11/04/2019
 ---
 
 > [!TIP]
-> Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/service/ui-concept-visual-interface) (preview), which provides drag-n-drop ML modules __plus__ scalability, version control, and enterprise security.
+> Customers currently using or evaluating Machine Learning Studio (classic) are encouraged to try [Azure Machine Learning designer](https://docs.microsoft.com/azure/machine-learning/concept-designer) (preview), which provides drag-n-drop ML modules __plus__ scalability, version control, and enterprise security.