fixing links

Author: Larry Franks

articles/machine-learning/how-to-deploy-model-cognitive-search.md

@@ -46,7 +46,7 @@ When deploying a model for use with Azure Cognitive Search, the deployment must
 
 * A registered model. If you do not have a model, use the example notebook at [https://github.com/Azure-Samples/azure-search-python-samples/tree/master/AzureML-Custom-Skill](https://github.com/Azure-Samples/azure-search-python-samples/tree/master/AzureML-Custom-Skill).
 
-* A general understanding of [How and where to deploy models](how-to-deploy-and-where.md).
+* A general understanding of [How and where to deploy models](v1/how-to-deploy-and-where.md).
 
 ## Connect to your workspace
 
@@ -185,7 +185,7 @@ def run(raw_data):
         return json.dumps({"error": result, "tb": traceback.format_exc()})
 ```
 
-For more information on entry scripts, see [How and where to deploy](how-to-deploy-and-where.md).
+For more information on entry scripts, see [How and where to deploy](v1/how-to-deploy-and-where.md).
 
 ## Define the software environment
 
@@ -213,7 +213,7 @@ For more information on environments, see [Create and manage environments for tr
 The deployment configuration defines the Azure Kubernetes Service hosting environment used to run the web service.
 
 > [!TIP]
-> If you aren't sure about the memory, CPU, or GPU needs of your deployment, you can use profiling to learn these. For more information, see [How and where to deploy a model](how-to-deploy-and-where.md).
+> If you aren't sure about the memory, CPU, or GPU needs of your deployment, you can use profiling to learn these. For more information, see [How and where to deploy a model](v1/how-to-deploy-and-where.md).
 
 ```python
 from azureml.core.model import Model

articles/machine-learning/how-to-machine-learning-interpretability-aml.md

@@ -365,7 +365,7 @@ Follow one of these paths to access the explanations dashboard in Azure Machine
    [![Visualization Dashboard with Aggregate Feature Importance in AzureML studio in experiments](./media/how-to-machine-learning-interpretability-aml/model-explanation-dashboard-aml-studio.png)](./media/how-to-machine-learning-interpretability-aml/model-explanation-dashboard-aml-studio.png#lightbox)
 
 * **Models** pane
-  1. If you registered your original model by following the steps in [Deploy models with Azure Machine Learning](./how-to-deploy-and-where.md), you can select **Models** in the left pane to view it.
+  1. If you registered your original model by following the steps in [Deploy models with Azure Machine Learning](v1/how-to-deploy-and-where.md), you can select **Models** in the left pane to view it.
   1. Select a model, and then the **Explanations** tab to view the explanations dashboard.
 
 ## Interpretability at inference time
@@ -430,7 +430,7 @@ You can deploy the explainer along with the original model and use it at inferen
 
 1. Deploy the image to a compute target, by following these steps:
 
-   1. If needed, register your original prediction model by following the steps in [Deploy models with Azure Machine Learning](./how-to-deploy-and-where.md).
+   1. If needed, register your original prediction model by following the steps in [Deploy models with Azure Machine Learning](v1/how-to-deploy-and-where.md).
 
    1. Create a scoring file.
 

articles/machine-learning/how-to-setup-customer-managed-keys.md

@@ -142,7 +142,7 @@ To use the key when deploying a model to Azure Container Instance, create a new
 For more information on creating and using a deployment configuration, see the following articles:
 
 * [AciWebservice.deploy_configuration()](/python/api/azureml-core/azureml.core.webservice.aci.aciwebservice#deploy-configuration-cpu-cores-none--memory-gb-none--tags-none--properties-none--description-none--location-none--auth-enabled-none--ssl-enabled-none--enable-app-insights-none--ssl-cert-pem-file-none--ssl-key-pem-file-none--ssl-cname-none--dns-name-label-none--primary-key-none--secondary-key-none--collect-model-data-none--cmk-vault-base-url-none--cmk-key-name-none--cmk-key-version-none-) reference
-* [Where and how to deploy](how-to-deploy-and-where.md)
+* [Where and how to deploy](v1/how-to-deploy-and-where.md)
 * [Deploy a model to Azure Container Instances](v1/how-to-deploy-azure-container-instance.md)
 
 For more information on using a customer-managed key with ACI, see [Encrypt data with a customer-managed key](../container-instances/container-instances-encrypt-data.md#encrypt-data-with-a-customer-managed-key).

articles/machine-learning/migrate-rebuild-web-service.md

@@ -23,7 +23,7 @@ Use Azure Machine Learning pipeline endpoints to make predictions, retrain model
 This article is part of the Studio (classic) to Azure Machine Learning migration series. For more information on migrating to Azure Machine Learning, see the [migration overview article](migrate-overview.md).
 
 > [!NOTE]
-> This migration series focuses on the drag-and-drop designer. For more information on deploying models programmatically, see [Deploy machine learning models in Azure](how-to-deploy-and-where.md).
+> This migration series focuses on the drag-and-drop designer. For more information on deploying models programmatically, see [Deploy machine learning models in Azure](v1/how-to-deploy-and-where.md).
 
 ## Prerequisites
 
@@ -55,7 +55,7 @@ There are multiple ways to deploy a model in Azure Machine Learning. One of the
 
     The designer converts the training pipeline into a real-time inference pipeline. A similar conversion also occurs in Studio (classic).
 
-    In the designer, the conversion step also [registers the trained model to your Azure Machine Learning workspace](how-to-deploy-and-where.md#registermodel).
+    In the designer, the conversion step also [registers the trained model to your Azure Machine Learning workspace](v1/how-to-deploy-and-where.md#registermodel).
 
 1. Select **Submit** to run the real-time inference pipeline, and verify that it runs successfully.
 
@@ -100,7 +100,7 @@ Use the following steps to publish a pipeline endpoint for batch prediction:
 
     The designer converts the training pipeline into a batch inference pipeline. A similar conversion also occurs in Studio (classic).
 
-    In the designer, this step also [registers the trained model to your Azure Machine Learning workspace](how-to-deploy-and-where.md#registermodel).
+    In the designer, this step also [registers the trained model to your Azure Machine Learning workspace](v1/how-to-deploy-and-where.md#registermodel).
 
 1. Select **Submit** to run the batch inference pipeline and verify that it successfully completes.
 

articles/machine-learning/v1/concept-model-management-and-deployment.md

@@ -10,7 +10,7 @@ author: rsethur
 ms.author:  seramasu
 ms.reviewer: larryfr
 ms.custom: seodec18, mktng-kw-nov2021, event-tier1-build-2022
-ms.date: 08/15/2022
+ms.date: 08/18/2022
 ---
 
 # MLOps: Model management, deployment, lineage, and monitoring with Azure Machine Learning v1
@@ -74,7 +74,7 @@ Registered models are identified by name and version. Each time you register a m
 > You can also register models trained outside Azure Machine Learning.
 
 You can't delete a registered model that is being used in an active deployment.
-For more information, see the register model section of [Deploy models](../how-to-deploy-and-where.md#registermodel).
+For more information, see the register model section of [Deploy models](how-to-deploy-and-where.md#registermodel).
 
 > [!IMPORTANT]
 > When using Filter by `Tags` option on the Models page of Azure Machine Learning Studio, instead of using `TagName : TagValue` customers should use `TagName=TagValue` (without space)
@@ -126,7 +126,7 @@ To deploy the model as a web service, you must provide the following items:
 * Dependencies required to use the model. For example, a script that accepts requests and invokes the model, conda dependencies, etc.
 * Deployment configuration that describes how and where to deploy the model.
 
-For more information, see [Deploy models](../how-to-deploy-and-where.md).
+For more information, see [Deploy models](how-to-deploy-and-where.md).
 
 #### Controlled rollout
 
@@ -205,7 +205,7 @@ You can also use Azure Data Factory to create a data ingestion pipeline that pre
 
 Learn more by reading and exploring the following resources:
 
-+ [How & where to deploy models](../how-to-deploy-and-where.md) with Azure Machine Learning
++ [How & where to deploy models](how-to-deploy-and-where.md) with Azure Machine Learning
 
 + [Tutorial: Train and deploy a model](../tutorial-train-deploy-notebook.md).
 

articles/machine-learning/v1/how-to-access-data.md

@@ -290,7 +290,7 @@ Azure Machine Learning provides several ways to use your models for scoring. Som
 | Method | Datastore access | Description |
 | ----- | :-----: | ----- |
 | [Batch prediction](../tutorial-pipeline-batch-scoring-classification.md) | ✔ | Make predictions on large quantities of data asynchronously. |
-| [Web service](../how-to-deploy-and-where.md) |   | Deploy models as a web service. |
+| [Web service](how-to-deploy-and-where.md) |   | Deploy models as a web service. |
 
 For situations where the SDK doesn't provide access to datastores, you might be able to create custom code by using the relevant Azure SDK to access the data. For example, the [Azure Storage SDK for Python](https://github.com/Azure/azure-storage-python) is a client library that you can use to access data stored in blobs or files.
 
@@ -304,4 +304,4 @@ Azure Data Factory provides efficient and resilient data transfer with more than
 
 * [Create an Azure machine learning dataset](how-to-create-register-datasets.md)
 * [Train a model](../how-to-set-up-training-targets.md)
-* [Deploy a model](../how-to-deploy-and-where.md)
+* [Deploy a model](how-to-deploy-and-where.md)