moving v1 articles to v1 folder

Author: Larry Franks

articles/machine-learning/how-to-debug-parallel-run-step.md renamed to articles/machine-learning/v1/how-to-debug-parallel-run-step.md

@@ -17,7 +17,7 @@ ms.date: 10/21/2021
 
 # Troubleshooting the ParallelRunStep
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, you learn how to troubleshoot when you get errors using the [ParallelRunStep](/python/api/azureml-pipeline-steps/azureml.pipeline.steps.parallel_run_step.parallelrunstep) class from the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro).
 

articles/machine-learning/how-to-debug-pipelines.md renamed to articles/machine-learning/v1/how-to-debug-pipelines.md

@@ -15,7 +15,7 @@ ms.custom: troubleshooting, devx-track-python, contperf-fy21q2, sdkv1, event-tie
 
 # Troubleshooting machine learning pipelines
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, you learn how to troubleshoot when you get errors running a [machine learning pipeline](concept-ml-pipelines.md) in the [Azure Machine Learning SDK](/python/api/overview/azure/ml/intro) and [Azure Machine Learning designer](./concept-designer.md). 
 
@@ -31,7 +31,7 @@ The following table contains common problems during pipeline development, with p
 | Pipeline not reusing steps | Step reuse is enabled by default, but ensure you haven't disabled it in a pipeline step. If reuse is disabled, the `allow_reuse` parameter in the step will be set to `False`. |
 | Pipeline is rerunning unnecessarily | To ensure that steps only rerun when their underlying data or scripts change, decouple your source-code directories for each step. If you use the same source directory for multiple steps, you may experience unnecessary reruns. Use the `source_directory` parameter on a pipeline step object to point to your isolated directory for that step, and ensure you aren't using the same `source_directory` path for multiple steps. |
 | Step slowing down over training epochs or other looping behavior | Try switching any file writes, including logging, from `as_mount()` to `as_upload()`. The **mount** mode uses a remote virtualized filesystem and uploads the entire file each time it is appended to. |
-| Compute target takes a long time to start | Docker images for compute targets are loaded from Azure Container Registry (ACR). By default, Azure Machine Learning creates an ACR that uses the *basic* service tier. Changing the ACR for your workspace to standard or premium tier may reduce the time it takes to build and load images. For more information, see [Azure Container Registry service tiers](../container-registry/container-registry-skus.md). |
+| Compute target takes a long time to start | Docker images for compute targets are loaded from Azure Container Registry (ACR). By default, Azure Machine Learning creates an ACR that uses the *basic* service tier. Changing the ACR for your workspace to standard or premium tier may reduce the time it takes to build and load images. For more information, see [Azure Container Registry service tiers](/azure/container-registry/container-registry-skus). |
 
 ### Authentication errors
 
@@ -233,7 +233,7 @@ For pipelines created in the designer, you can find the **70_driver_log** file i
 
 ### Enable logging for real-time endpoints
 
-In order to troubleshoot and debug real-time endpoints in the designer, you must enable Application Insight logging using the SDK. Logging lets you troubleshoot and debug model deployment and usage issues. For more information, see [Logging for deployed models](./v1/how-to-enable-app-insights.md). 
+In order to troubleshoot and debug real-time endpoints in the designer, you must enable Application Insight logging using the SDK. Logging lets you troubleshoot and debug model deployment and usage issues. For more information, see [Logging for deployed models](how-to-enable-app-insights.md). 
 
 ### Get logs from the authoring page
 

articles/machine-learning/how-to-deploy-pipelines.md renamed to articles/machine-learning/v1/how-to-deploy-pipelines.md

@@ -14,15 +14,15 @@ ms.custom: contperf-fy21q1, sdkv1, event-tier1-build-2022
 
 # Publish and track machine learning pipelines
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 This article will show you how to share a machine learning pipeline with your colleagues or customers.
 
 Machine learning pipelines are reusable workflows for machine learning tasks. One benefit of pipelines is increased collaboration. You can also version pipelines, allowing customers to use the current model while you're working on a new version. 
 
 ## Prerequisites
 
-* Create an [Azure Machine Learning workspace](quickstart-create-resources.md) to hold all your pipeline resources
+* Create an [Azure Machine Learning workspace](../quickstart-create-resources.md) to hold all your pipeline resources
 
 * [Configure your development environment](how-to-configure-environment.md) to install the Azure Machine Learning SDK, or use an [Azure Machine Learning compute instance](concept-compute-instance.md) with the SDK already installed
 

articles/machine-learning/how-to-log-pipelines-application-insights.md renamed to articles/machine-learning/v1/how-to-log-pipelines-application-insights.md

@@ -14,21 +14,21 @@ ms.custom: devx-track-python, sdkv1, event-tier1-build-2022
 
 # Collect machine learning pipeline log files in Application Insights for alerts and debugging
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 The [OpenCensus](https://opencensus.io/quickstart/python/) Python library can be used to route logs to Application Insights from your scripts. Aggregating logs from pipeline runs in one place allows you to build queries and diagnose issues. Using Application Insights will allow you to track logs over time and compare pipeline logs across runs.
 
 Having your logs in once place will provide a history of exceptions and error messages. Since Application Insights integrates with Azure Alerts, you can also create alerts based on Application Insights queries.
 
 ## Prerequisites
 
-* Follow the steps to create an [Azure Machine Learning workspace](quickstart-create-resources.md) and [create your first pipeline](./how-to-create-machine-learning-pipelines.md)
+* Follow the steps to create an [Azure Machine Learning workspace](../quickstart-create-resources.md) and [create your first pipeline](./how-to-create-machine-learning-pipelines.md)
 * [Configure your development environment](./how-to-configure-environment.md) to install the Azure Machine Learning SDK.
 * Install the [OpenCensus Azure Monitor Exporter](https://pypi.org/project/opencensus-ext-azure/) package locally:
   ```python
   pip install opencensus-ext-azure
   ```
-* Create an [Application Insights instance](../azure-monitor/app/opencensus-python.md) (this doc also contains information on getting the connection string for the resource)
+* Create an [Application Insights instance](/azure/azure-monitor/app/opencensus-python) (this doc also contains information on getting the connection string for the resource)
 
 ## Getting Started
 
@@ -160,6 +160,6 @@ Some of the queries below use 'customDimensions.Level'. These severity levels co
 
 ## Next Steps
 
-Once you have logs in your Application Insights instance, they can be used to set [Azure Monitor alerts](../azure-monitor/alerts/alerts-overview.md) based on query results.
+Once you have logs in your Application Insights instance, they can be used to set [Azure Monitor alerts](/azure/azure-monitor/alerts/alerts-overview) based on query results.
 
-You can also add results from queries to an [Azure Dashboard](../azure-monitor/app/tutorial-app-dashboards.md#add-logs-query) for additional insights.
+You can also add results from queries to an [Azure Dashboard](/azure/azure-monitor/app/tutorial-app-dashboards#add-logs-query) for additional insights.

articles/machine-learning/how-to-move-data-in-out-of-pipelines.md renamed to articles/machine-learning/v1/how-to-move-data-in-out-of-pipelines.md

@@ -7,15 +7,15 @@ ms.service: machine-learning
 ms.subservice: mldata
 ms.author: larryfr
 author: blackmist
-ms.date: 10/21/2021
+ms.date: 08/18/2022
 ms.topic: how-to
 ms.custom: contperf-fy20q4, devx-track-python, data4ml, sdkv1, event-tier1-build-2022
 #Customer intent: As a data scientist using Python, I want to get data into my pipeline and flowing between steps.
 ---
 
 # Moving data into and between ML pipeline steps (Python)
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 This article provides code for importing, transforming, and moving data between steps in an Azure Machine Learning pipeline. For an overview of how data works in Azure Machine Learning, see [Access data in Azure storage services](how-to-access-data.md). For the benefits and structure of Azure Machine Learning pipelines, see [What are Azure Machine Learning pipelines?](concept-ml-pipelines.md)
 
@@ -47,7 +47,7 @@ You'll need:
    ws = Workspace.from_config()
    ```
 
-- Some pre-existing data. This article briefly shows the use of an [Azure blob container](../storage/blobs/storage-blobs-overview.md).
+- Some pre-existing data. This article briefly shows the use of an [Azure blob container](/azure/storage/blobs/storage-blobs-overview).
 
 - Optional: An existing machine learning pipeline, such as the one described in [Create and run machine learning pipelines with Azure Machine Learning SDK](./how-to-create-machine-learning-pipelines.md).
 
@@ -69,13 +69,13 @@ datastore_path = [
 cats_dogs_dataset = Dataset.File.from_files(path=datastore_path)
 ```
 
-For more options on creating datasets with different options and from different sources, registering them and reviewing them in the Azure Machine Learning UI, understanding how data size interacts with compute capacity, and versioning them, see [Create Azure Machine Learning datasets](./v1/how-to-create-register-datasets.md). 
+For more options on creating datasets with different options and from different sources, registering them and reviewing them in the Azure Machine Learning UI, understanding how data size interacts with compute capacity, and versioning them, see [Create Azure Machine Learning datasets](how-to-create-register-datasets.md). 
 
 ### Pass datasets to your script
 
 To pass the dataset's path to your script, use the `Dataset` object's `as_named_input()` method. You can either pass the resulting `DatasetConsumptionConfig` object to your script as an argument or, by using the `inputs` argument to your pipeline script, you can retrieve the dataset using `Run.get_context().input_datasets[]`.
 
-Once you've created a named input, you can choose its access mode: `as_mount()` or `as_download()`. If your script processes all the files in your dataset and the disk on your compute resource is large enough for the dataset, the download access mode is the better choice. The download access mode will avoid the overhead of streaming the data at runtime. If your script accesses a subset of the dataset or it's too large for your compute, use the mount access mode. For more information, read [Mount vs. Download](./how-to-train-with-datasets.md#mount-vs-download)
+Once you've created a named input, you can choose its access mode: `as_mount()` or `as_download()`. If your script processes all the files in your dataset and the disk on your compute resource is large enough for the dataset, the download access mode is the better choice. The download access mode will avoid the overhead of streaming the data at runtime. If your script accesses a subset of the dataset or it's too large for your compute, use the mount access mode. For more information, read [Mount vs. Download](how-to-train-with-datasets.md#mount-vs-download)
 
 To pass a dataset to your pipeline step:
 
@@ -194,7 +194,7 @@ After the initial pipeline step writes some data to the `OutputFileDatasetConfig
 
 In the following code: 
 
-* `step1_output_data` indicates that the output of the PythonScriptStep, `step1` is written to the ADLS Gen 2 datastore, `my_adlsgen2` in upload access mode. Learn more about how to [set up role permissions](./v1/how-to-access-data.md) in order to write data back to ADLS Gen 2 datastores. 
+* `step1_output_data` indicates that the output of the PythonScriptStep, `step1` is written to the ADLS Gen 2 datastore, `my_adlsgen2` in upload access mode. Learn more about how to [set up role permissions](how-to-access-data.md) in order to write data back to ADLS Gen 2 datastores. 
 
 * After `step1` completes and the output is written to the destination indicated by `step1_output_data`, then step2 is ready to use `step1_output_data` as an input. 
 
@@ -236,12 +236,12 @@ step1_output_ds = step1_output_data.register_on_complete(name='processed_data',
 Azure does not automatically delete intermediate data written with `OutputFileDatasetConfig`. To avoid storage charges for large amounts of unneeded data, you should either:
 
 * Programmatically delete intermediate data at the end of a pipeline job, when it is no longer needed
-* Use blob storage with a short-term storage policy for intermediate data (see [Optimize costs by automating Azure Blob Storage access tiers](../storage/blobs/lifecycle-management-overview.md?tabs=azure-portal)) 
+* Use blob storage with a short-term storage policy for intermediate data (see [Optimize costs by automating Azure Blob Storage access tiers](/azure/storage/blobs/lifecycle-management-overview)) 
 * Regularly review and delete no-longer-needed data
 
 For more information, see [Plan and manage costs for Azure Machine Learning](concept-plan-manage-cost.md).
 
 ## Next steps
 
-* [Create an Azure machine learning dataset](./v1/how-to-create-register-datasets.md)
-* [Create and run machine learning pipelines with Azure Machine Learning SDK](./how-to-create-machine-learning-pipelines.md)
+* [Create an Azure machine learning dataset](how-to-create-register-datasets.md)
+* [Create and run machine learning pipelines with Azure Machine Learning SDK](how-to-create-machine-learning-pipelines.md)

articles/machine-learning/how-to-trigger-published-pipeline.md renamed to articles/machine-learning/v1/how-to-trigger-published-pipeline.md

@@ -15,7 +15,7 @@ ms.custom: devx-track-python, sdkv1, event-tier1-build-2022
 
 # Trigger machine learning pipelines
 
-[!INCLUDE [sdk v1](../../includes/machine-learning-sdk-v1.md)]
+[!INCLUDE [sdk v1](../../../includes/machine-learning-sdk-v1.md)]
 
 In this article, you'll learn how to programmatically schedule a pipeline to run on Azure. You can create a schedule based on elapsed time or on file-system changes. Time-based schedules can be used to take care of routine tasks, such as monitoring for data drift. Change-based schedules can be used to react to irregular or unpredictable changes, such as new data being uploaded or old data being edited. After learning how to create schedules, you'll learn how to retrieve and deactivate them. Finally, you'll learn how to use other Azure services, Azure Logic App and Azure Data Factory, to run pipelines. An Azure Logic App allows for more complex triggering logic or behavior. Azure Data Factory pipelines allow you to call a machine learning pipeline as part of a larger data orchestration pipeline.
 
@@ -141,7 +141,7 @@ If you then run `Schedule.list(ws)` again, you should get an empty list.
 
 ## Use Azure Logic Apps for complex triggers 
 
-More complex trigger rules or behavior can be created using an [Azure Logic App](../logic-apps/logic-apps-overview.md).
+More complex trigger rules or behavior can be created using an [Azure Logic App](/azure/logic-apps/logic-apps-overview).
 
 To use an Azure Logic App to trigger a Machine Learning pipeline, you'll need the REST endpoint for a published Machine Learning pipeline. [Create and publish your pipeline](./how-to-create-machine-learning-pipelines.md). Then find the REST endpoint of your `PublishedPipeline` by using the pipeline ID:
 
@@ -154,11 +154,11 @@ published_pipeline.endpoint
 
 ## Create a Logic App
 
-Now create an [Azure Logic App](../logic-apps/logic-apps-overview.md) instance. If you wish, [use an integration service environment (ISE)](../logic-apps/connect-virtual-network-vnet-isolated-environment.md) and [set up a customer-managed key](../logic-apps/customer-managed-keys-integration-service-environment.md) for use by your Logic App.
+Now create an [Azure Logic App](../logic-apps/logic-apps-overview.md) instance. If you wish, [use an integration service environment (ISE)](/azure/logic-apps/connect-virtual-network-vnet-isolated-environment) and [set up a customer-managed key](/azure/logic-apps/customer-managed-keys-integration-service-environment) for use by your Logic App.
 
 Once your Logic App has been provisioned, use these steps to configure a trigger for your pipeline:
 
-1. [Create a system-assigned managed identity](../logic-apps/create-managed-service-identity.md) to give the app access to your Azure Machine Learning Workspace.
+1. [Create a system-assigned managed identity](/azure/logic-apps/create-managed-service-identity) to give the app access to your Azure Machine Learning Workspace.
 
 1. Navigate to the Logic App Designer view and select the Blank Logic App template. 
     > [!div class="mx-imgBorder"]