Fix bad links

jonburchel · jonburchel · commit 0c0678c67a94 · 2021-11-19T12:50:11.000-05:00
diff --git a/articles/data-factory/compute-linked-services.md b/articles/data-factory/compute-linked-services.md
@@ -15,7 +15,7 @@ ms.custom: devx-track-azurepowershell, synapse
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 This article explains different compute environments that you can use to process or transform data. It also provides details about different configurations (on-demand vs. bring your own) supported when configuring linked services linking these compute environments.
 
diff --git a/articles/data-factory/concepts-pipelines-activities.md b/articles/data-factory/concepts-pipelines-activities.md
@@ -18,7 +18,7 @@ ms.date: 09/09/2021
 > * [Current version](concepts-pipelines-activities.md)
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 This article helps you understand pipelines and activities in Azure Data Factory and Azure Synapse Analytics and use them to construct end-to-end data-driven workflows for your data movement and data processing scenarios.
 
diff --git a/articles/data-factory/transform-data-using-machine-learning.md b/articles/data-factory/transform-data-using-machine-learning.md
@@ -19,7 +19,7 @@ ms.date: 09/09/2021
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!NOTE] Since Machine Learning Studio (classic) resources can no longer be created after 1 Dec, 2021, users are encouraged to use [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/) with the [Machine Learning Execute Pipeline activity](transform-data-machine-learning-service.md) rather than using the Batch Execution activity to execute Machine Learning Studio (classic) batches.
 
diff --git a/articles/data-factory/transform-data.md b/articles/data-factory/transform-data.md
@@ -31,7 +31,7 @@ ms.date: 09/09/2021
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 ## Overview
 This article explains data transformation activities in Azure Data Factory and Synapse pipelines that you can use to transform and process your raw data into predictions and insights at scale. A transformation activity executes in a computing environment such as Azure Databricks or Azure HDInsight. It provides links to articles with detailed information on each transformation activity.
diff --git a/articles/data-factory/update-machine-learning-models.md b/articles/data-factory/update-machine-learning-models.md
@@ -16,7 +16,7 @@ ms.date: 09/09/2021
 
 [!INCLUDE[appliesto-adf-asa-md](includes/appliesto-adf-asa-md.md)]
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!NOTE] Since Machine Learning Studio (classic) resources can no longer be created after 1 Dec, 2021, users are encouraged to use [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/) with the [Machine Learning Execute Pipeline activity](transform-data-machine-learning-service.md) rather than using the Update Resource activity to update Machine Learning Studio (classic) models.
 
diff --git a/articles/data-factory/v1/data-factory-azure-ml-batch-execution-activity.md b/articles/data-factory/v1/data-factory-azure-ml-batch-execution-activity.md
@@ -12,7 +12,7 @@ ms.date: 10/22/2021
 
 # Create predictive pipelines using Machine Learning Studio (classic) and Azure Data Factory
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!div class="op_single_selector" title1="Transformation Activities"]
 > * [Hive Activity](data-factory-hive-activity.md)
diff --git a/articles/data-factory/v1/data-factory-azure-ml-update-resource-activity.md b/articles/data-factory/v1/data-factory-azure-ml-update-resource-activity.md
@@ -12,7 +12,7 @@ ms.date: 10/22/2021
 
 # Updating ML Studio (classic) models using Update Resource Activity
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!div class="op_single_selector" title1="Transformation Activities"]
 > * [Hive Activity](data-factory-hive-activity.md) 
diff --git a/articles/data-factory/v1/data-factory-compute-linked-services.md b/articles/data-factory/v1/data-factory-compute-linked-services.md
@@ -12,7 +12,7 @@ ms.date: 10/22/2021
 
 # Compute environments supported by Azure Data Factory version 1
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!NOTE]
 > This article applies to version 1 of Azure Data Factory. If you are using the current version of the Data Factory service, see [Compute linked services in](../compute-linked-services.md).
diff --git a/articles/data-factory/v1/data-factory-data-transformation-activities.md b/articles/data-factory/v1/data-factory-data-transformation-activities.md
@@ -12,7 +12,7 @@ ms.date: 10/22/2021
 
 # Transform data in Azure Data Factory version 1
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 > [!div class="op_single_selector"]
 > * [Hive](data-factory-hive-activity.md)  
diff --git a/articles/data-factory/v1/data-factory-json-scripting-reference.md b/articles/data-factory/v1/data-factory-json-scripting-reference.md
@@ -15,7 +15,7 @@ ms.custom: devx-track-azurepowershell
 > [!NOTE]
 > This article applies to version 1 of Data Factory.
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 This article provides JSON schemas and examples for defining Azure Data Factory entities (pipeline, activity, dataset, and linked service).
 
diff --git a/articles/data-factory/v1/data-factory-samples.md b/articles/data-factory/v1/data-factory-samples.md
@@ -14,7 +14,7 @@ ms.date: 10/22/2021
 > [!NOTE]
 > This article applies to version 1 of Data Factory. If you are using the current version of the Data Factory service, see [PowerShell samples in Data Factory](../samples-powershell.md) and [code samples in the Azure Code Samples gallery](https://azure.microsoft.com/resources/samples/?service=data-factory).
 
-[!INCLUDE[ML Studio (classic) retirement](../../includes/machine-learning-studio-classic-deprecation.md)] 
+[!INCLUDE[ML Studio (classic) retirement](/includes/machine-learning-studio-classic-deprecation.md)] 
 
 ## Samples on GitHub
 The [GitHub Azure-DataFactory repository](https://github.com/azure/azure-datafactory) contains several samples that help you quickly ramp up with Azure Data Factory service (or) modify the scripts and use it in own application. The Samples\JSON folder contains JSON snippets for common scenarios.
