remove unused file

Author: sdgilley

articles/machine-learning/.openpublishing.redirection.machine-learning.json

@@ -630,9 +630,14 @@
         "redirect_url": "https://azure.microsoft.com/updates/?product=machine-learning-studio",
         "redirect_document_id": false
     },
+    {
+        "source_path_from_root": "/articles/machine-learning/studio/overview-what-is-machine-learning-studio.md",
+        "redirect_url": "/azure/machine-learning/overview-what-is-azure-machine-learning#studio",
+        "redirect_document_id": false
+    },
     {
         "source_path_from_root": "/articles/machine-learning/studio/what-is-ml-studio.md",
-        "redirect_url": "/azure/machine-learning/overview-what-is-machine-learning-studio#ml-studio-classic-vs-azure-machine-learning-studio",
+        "redirect_url": "/azure/machine-learning/overview-what-is-azure-machine-learning#studio",
         "redirect_document_id": false
     },
     {
@@ -2772,7 +2777,7 @@
     },
     {
         "source_path_from_root": "/articles/machine-learning/compare-azure-ml-to-studio-classic.md",
-        "redirect_url": "/azure/machine-learning/overview-what-is-machine-learning-studio#ml-studio-classic-vs-azure-machine-learning-studio",
+        "redirect_url": "/azure/machine-learning/migrate-overview",
         "redirect_document_id": false
     },
     {

articles/machine-learning/concept-responsible-ai-dashboard.md

@@ -40,7 +40,7 @@ The Responsible AI dashboard is accompanied by a [PDF scorecard](how-to-responsi
 
 ## Responsible AI dashboard components
 
-The Responsible AI dashboard brings together, in a comprehensive view, various new and pre-existing tools. The dashboard integrates these tools with [Azure Machine Learning CLI v2, Azure Machine Learning Python SDK v2](concept-v2.md), and [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md). The tools include:  
+The Responsible AI dashboard brings together, in a comprehensive view, various new and pre-existing tools. The dashboard integrates these tools with [Azure Machine Learning CLI v2, Azure Machine Learning Python SDK v2](concept-v2.md), and [Azure Machine Learning studio](overview-what-is-azure-machine-learning.md#studio). The tools include:  
 
 - [Data explorer](concept-data-analysis.md), to understand and explore your dataset distributions and statistics.
 - [Model overview and fairness assessment](concept-fairness-ml.md), to evaluate the performance of your model and evaluate your model's group fairness issues (how your model's predictions affect diverse groups of people).

articles/machine-learning/how-to-train-mlflow-projects.md

@@ -154,7 +154,7 @@ dependencies:
 
 Submit the local run and ensure you set the parameter `backend = "azureml" `. With this setting, you can submit runs locally and get the added support of automatic output tracking, log files, snapshots, and printed errors in your workspace.
 
-View your runs and metrics in the [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md).
+View your runs and metrics in the [Azure Machine Learning studio](https://ml.azure.com).
 
 ```python
 local_env_run = mlflow.projects.run(uri=".", 
@@ -198,7 +198,7 @@ dependencies:
 
 Submit the mlflow project run and ensure you set the parameter `backend = "azureml" `. With this setting, you can submit your run to your remote compute and get the added support of automatic output tracking, log files, snapshots, and printed errors in your workspace.
 
-View your runs and metrics in the [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md).
+View your runs and metrics in the [Azure Machine Learning studio](https://ml.azure.com).
 
 ```python
 remote_mlflow_run = mlflow.projects.run(uri=".", 

articles/machine-learning/how-to-understand-automated-ml.md

@@ -38,7 +38,7 @@ For example, automated ML generates the following charts based on experiment typ
 ## View job results
 
 After your automated ML experiment completes, a history of the jobs can be found via:
-  - A browser with [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md)
+  - A browser with [Azure Machine Learning studio](https://ml.azure.com)
   - A Jupyter notebook using the [JobDetails Jupyter widget](/python/api/azureml-widgets/azureml.widgets.rundetails)
 
 The following steps and video, show you how to view the run history and model evaluation metrics and charts in the studio:

articles/machine-learning/how-to-use-automated-ml-for-ml-models.md

@@ -15,7 +15,7 @@ ms.custom: automl, FY21Q4-aml-seo-hack, contperf-fy21q4, event-tier1-build-2022,
 
 # Set up no-code AutoML training with the studio UI 
 
-In this article, you learn how to set up AutoML training jobs without a single line of code using Azure Machine Learning automated ML in the [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md).
+In this article, you learn how to set up AutoML training jobs without a single line of code using Azure Machine Learning automated ML in the [Azure Machine Learning studio](overview-what-is-azure-machine-learning.md#studio.md).
 
 Automated machine learning, AutoML, is a process in which the best machine learning algorithm to use for your specific data is selected for you. This process enables you to generate machine learning models quickly. [Learn more about how Azure Machine Learning implements automated machine learning](concept-automated-ml.md).
 

articles/machine-learning/how-to-use-mlflow-cli-runs.md

@@ -277,7 +277,7 @@ To register and view a model from a job, use the following steps:
     mlflow.register_model(model_uri,"registered_model_name")
     ```
 
-1. View the registered model in your workspace with [Azure Machine Learning studio](overview-what-is-machine-learning-studio.md).
+1. View the registered model in your workspace with [Azure Machine Learning studio](https://ml.azure.com).
 
     In the following example the registered model, `my-model` has MLflow tracking metadata tagged. 
 

articles/machine-learning/overview-what-is-machine-learning-studio.md

@@ -260,7 +260,7 @@ Azure Machine Learning provides the following monitoring and logging capabilitie
 
 ### Studio
 
-[Azure Machine Learning studio](../overview-what-is-machine-learning-studio.md) provides a web view of all the artifacts in your workspace.  You can view results and details of your datasets, experiments, pipelines, models, and endpoints.  You can also manage compute resources and datastores in the studio.
+[Azure Machine Learning studio](../overview-what-is-azure-machine-learning.md#studio.md) provides a web view of all the artifacts in your workspace.  You can view results and details of your datasets, experiments, pipelines, models, and endpoints.  You can also manage compute resources and datastores in the studio.
 
 The studio is also where you access the interactive tools that are part of Azure Machine Learning:
 

articles/machine-learning/v1/concept-azure-machine-learning-architecture.md

@@ -16,7 +16,7 @@ ms.custom: data4ml, event-tier1-build-2022, ignite-2022
 
 # Connect to data with the Azure Machine Learning studio
 
-In this article, learn how to access your data with the [Azure Machine Learning studio](../overview-what-is-machine-learning-studio.md). Connect to your data in storage services on Azure with [Azure Machine Learning datastores](how-to-access-data.md), and then package that data for tasks in your ML workflows with [Azure Machine Learning datasets](how-to-create-register-datasets.md).
+In this article, learn how to access your data with the [Azure Machine Learning studio](https://ml.azure.com). Connect to your data in storage services on Azure with [Azure Machine Learning datastores](how-to-access-data.md), and then package that data for tasks in your ML workflows with [Azure Machine Learning datasets](how-to-create-register-datasets.md).
 
 The following table defines and summarizes the benefits of datastores and datasets. 
 

articles/machine-learning/v1/how-to-connect-data-ui.md

@@ -324,7 +324,7 @@ To register and view a model from a run, use the following steps:
     mlflow.register_model(model_uri,"registered_model_name")
     ```
 
-1. View the registered model in your workspace with [Azure Machine Learning studio](../overview-what-is-machine-learning-studio.md).
+1. View the registered model in your workspace with [Azure Machine Learning studio](https://ml.azure.com).
 
     In the following example the registered model, `my-model` has MLflow tracking metadata tagged.