moving v1 articles to v1 folder

Author: Larry Franks

articles/machine-learning/how-to-train-keras.md

@@ -180,7 +180,7 @@ It's now time to submit the job to run in AzureML. This time, you'll use `create
 Once completed, the job will register a model in your workspace (as a result of training) and output a link for viewing the job in AzureML studio.
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory.
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory.
 
 ### What happens during job execution
 As the job is executed, it goes through the following stages:

articles/machine-learning/how-to-train-pytorch.md

@@ -140,7 +140,7 @@ It's now time to submit the job to run in AzureML. This time, you'll use `create
 Once completed, the job will register a model in your workspace (as a result of training) and output a link for viewing the job in AzureML studio.
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory.
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory.
 
 ### What happens during job execution
 As the job is executed, it goes through the following stages:

articles/machine-learning/how-to-train-scikit-learn.md

@@ -164,7 +164,7 @@ It's now time to submit the job to run in AzureML. This time you'll use `create_
 Once completed, the job will register a model in your workspace (as a result of training) and output a link for viewing the job in AzureML studio.
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory.
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory.
 
 ### What happens during job execution
 As the job is executed, it goes through the following stages:

articles/machine-learning/how-to-train-tensorflow.md

@@ -166,7 +166,7 @@ It's now time to submit the job to run in AzureML. This time, you'll use `create
 Once completed, the job will register a model in your workspace (as a result of training) and output a link for viewing the job in AzureML studio.
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory.
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory.
 
 ### What happens during job execution
 As the job is executed, it goes through the following stages:

articles/machine-learning/how-to-train-with-custom-image.md

@@ -171,7 +171,7 @@ run.wait_for_completion(show_output=True)
 ```
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use an [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory. Instead, access your data by using a [datastore](/python/api/azureml-core/azureml.data).
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use an [.ignore file](concept-train-machine-learning-model.md#understand-what-happens-when-you-submit-a-training-job) or don't include it in the source directory. Instead, access your data by using a [datastore](/python/api/azureml-core/azureml.data).
 
 ## Next steps
 In this article, you trained a model by using a custom Docker image. See these other articles to learn more about Azure Machine Learning:

articles/machine-learning/how-to-save-write-experiment-files.md renamed to articles/machine-learning/v1/how-to-save-write-experiment-files.md

@@ -211,7 +211,7 @@ src = ScriptRunConfig(source_directory=project_folder,
 ```
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](../how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
 
 For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
 

articles/machine-learning/v1/how-to-train-pytorch.md

@@ -141,7 +141,7 @@ run.wait_for_completion(show_output=True)
 ```
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](../how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
 
 ### What happens during run execution
 As the run is executed, it goes through the following stages:

articles/machine-learning/v1/how-to-train-scikit-learn.md

@@ -222,7 +222,7 @@ src = ScriptRunConfig(source_directory=script_folder,
 ```
 
 > [!WARNING]
-> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](../how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
+> Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use a [.ignore file](how-to-save-write-experiment-files.md#storage-limits-of-experiment-snapshots) or don't include it in the source directory . Instead, access your data using an Azure ML [dataset](how-to-train-with-datasets.md).
 
 For more information on configuring jobs with ScriptRunConfig, see [Configure and submit training runs](how-to-set-up-training-targets.md).
 

articles/machine-learning/v1/how-to-train-tensorflow.md

@@ -166,7 +166,7 @@
     href: how-to-use-environments.md
   - name: Set input & output directories
     displayName: large data, write, experiment files, size limit
-    href: ../how-to-save-write-experiment-files.md
+    href: how-to-save-write-experiment-files.md
   - name: Use private Python packages
     displayName: pip, Conda, anaconda
     href: how-to-use-private-python-packages.md